Artificial Intelligence Strategies & Policies Reviewed. How do we humans perceive AI?; Are we allergic to AI?, Have AI aversions? or do we love AI or is it hate? Maybe indifference? What about trust? More or is it less than in our peers? How to shape your Corporate AI Policy; How to formulate your Corporate AI Strategy. Lots of questions. Many answers leading to more questions.
“It is better that ten guilty persons escape, than that one innocent party suffer.”, Sir William Blackstone (1765) paraphrased.
Machines mess up. Humans even more so. The latter can be difficult, even impossible, to really understand. The former is a bit more straightforward. This short essay describes how we can get an idea of some of the root causes of machine model errors. Particular as those machine model errors relate to group bias and unfairness. Its elementary really, as John Lee Miller would say. Look at your model’s confusion defined by its false positives and negatives as well as its true results. Reflect on this overall as well as for well-defined groups that exist within your sample population under study. My intention is to point out (the obvious maybe?) that the variations in each of your attributes, feed into your learning machine model, will determine the level of confusion that your model ultimately will have towards individual groups within your larger population under study. Model confusion that may cause group biases and unfair treatment of minority groups lost in the resolution of your data and chosen attributes.
Intelligent machines made in our image in our world.
We humans are cursed by an immense amount of cognitive biases clouding our judgments and actions. Maybe we are also blessed by for most parts of life being largely ignorant of those same biases. We readily forgive our fellow humans mistakes. Even grave ones. We frequently ignore or are unaware of our own mistakes. However, we hold machines to much stricter standards than our fellow humans. From machines we expect perfection. From humans? … well the story is quite the opposite.
Algorithmic fairness, bias, explainability and ethical aspects of machine learning are hot and popular topics. Unfortunately, maybe more so in academia than elsewhere. But that is changing too. Experts, frequently academic scholars, are warning us that AI fairness is not guarantied even as recommendations and policy outcomes are being produced by non-human means. We do not avoid biased decisions or unfair actions by replacing our wet biological carbon-based brains, subject to tons of cognitive biases, with another substrate for computation and decision making that is subjected to information coming from a fundamentally biased society. Far from it.
Bias and unfairness can be present (or introduced) at many stages of a machine learning process. Much of the data we use for our machine learning models reflect society’s good, bad and ugly sides. For example, data being used to train a given algorithmic model could be biased (or unfair) either because it reflect a fundamentally biased or unfair partition of subject matter under study or because in the data preparation process the data have become biased (intentionally or un-intentionally). Most of us understand the concept of GiGo (i.e., “Garbage in Garbage out”). The quality of your model output, or computation, is reflected by the quality of your input. Unless corrected (often easier said than done) it is understandable that an outcome of a machine learning model may be biased or fundamentally unfair, if the data input was flawed. Likewise, the machine learning architecture and model may also introduce (intentional as well as un-intentional) biases or unfair results even if the original training data would have been unbiased and fair.
At this point, you should get a bit uneasy (or impatient). I haven’t really told you what I actually mean by bias or unfairness. While there are 42 (i.e., many, but 42 is the answer to many things unknown and known) definitions out there defining fairness (or bias), I will define it as “a systematic and significant difference in outcome of a given policy between distinct and statistically meaningful groups” (note that in case of in-group systematic bias it often means that there actually are distinct sub-groups within that main group). So, yes this is a challenge.
How “confused” is your learned machine model?
When I am exploring outcomes (or policy recommendations) of my machine learning models, I spend a fair amount of time on trying to understand the nature of my false positives (i.e., predicted positive outcomes that should have been negative) as well as false negatives (predicted negative outcomes that should have been positive). My tool of choice is the so-called confusion matrix (i.e., see figure below) which summarizes your machine learning model’s performance in terms of its accuracy as well as inability of predicting outcomes. It is a simple construction. It is also very powerful.
The above figure provides a confusion matrix example of a loan policy subjected to machine learning. We have
TRUE NEGATIVE (Light Blue color): Model suggest that the loan application should be rejected consistent with the actual outcome of the loan being rejected. This outcome is a loss mitigating measure and should be weighted against new business versus the risk of default providing a loan.
FALSE POSITIVE (Yellow color): Model suggest that the loan application should be approved in opposition to the actual outcome of the loan being rejected. Note once this model would be operational this may lead to increased risk of financial loss to the business offering the loans that the applicant is likely to default on. May also lead to negative socio-economical impact to the individuals that are offered a loan they may not be able to pay back.
FALSE NEGATIVE (Red color): Model suggest that the loan application should be rejected in opposition with the actual outcome of the loan being accepted. Note once this model would be operational this may lead to loss of business by rejecting a loan application that otherwise would have had a high likelihood of being payed back. Also may lead to negative socio-economical impact to the individuals being rejected due to lost opportunities for individuals and community.
TRUE POSITIVE (Green color): Model suggest that the loan application should be approved consistent with the actual outcome of the loan being approved. This provides for new business opportunities and increased topline within an acceptable risk level.
The confusion matrix will identify the degree of bias or unfairness that your machine learning model introduces between groups (or segments) in your business processes and in your corporate decision making.
The following example (below) illustrates how the confusion matrix varies with changes to a group’s attributes distributions, e.g., variance differences (or standard deviation), mean value differences, etc..
What is obvious from the above illustration is that policy outcome on a group basis is (very) sensitive to the attribute’s distribution properties between those groups. Variations in the attributes between groups can illicit biases that ultimately may lead to unfairness between groups but also within a defined group.
Thus, the confusion matrix leads us back to your chosen attributes (or features), their statistical distributions, the quality of your data or measurements that make up those distributions. If your product or app or policy applies to many different groups, you better understand whether those groups are treated the same, good or bad. Or … if you intend to differentiate between groups, you may want to be (reasonably) sure that no unintended bad consequences will negatively expose your business model or policy.
A word of caution: even if the confusion matrix gives your model “green light” for production, you cannot by default assume that the results produced may not result in systematic group bias and ultimately unfairness against minority groups. Moreover, in real-world implementations it is unlikely to completely free your machine models from errors that may lead to a certain degree of systematic bias and unfairness (however small).
So, let’s say that I have a particular policy outcome that I would like to check whether it is biased (and possible unfair) against certain defined groups (e.g., men & women). Let’s also assume that the intention with the given policy was to have a fair and unbiased outcome without group dependency (e.g., independence of race, gender, sexual orientation, etc.). The policy outcome is derived from a number of attributes (or features) deemed important but excludes obvious attributes that is thought likely to cause the policy to systematically bias towards or against certain groups (e.g., women). In order for your machine model to perform well it needs in general lots of relevant data (rather than Big Data). For each individual, in your population (under study), you will gather data for the attributes deemed relevant for your model (and maybe some that you don’t think matters). Each attribute can be represented by a statistical distribution reflecting the variation within the population or groups under study. It will often be the case that an attribute’s distribution will be fairly similar between different groups. Either because it really is slightly different for different groups or because your data “sucks” (e.g., due to poor quality, too little to resolve subtle differences, etc… ).
If a policy is supposed to be unbiased, I should not be able to predict with any (statistical) confidence which group a policy taker belongs to, given the policy outcome and the attributes used to derive the policy. Or in other words, I should not be able to do better than what chance (or base rate) would dictate.
For each attribute (or feature), deemed important for our machine learning model, we either have, or we collect, lots of data. Furthermore, for each of the considered attributes we will have a distribution represented by a mean value and a variance (and high order moments of the distribution such as skewness, i.e., the asymmetry around the mean and kurtosis, i.e., the shape of distributions tails). Comparing two (or more) groups we should be interested in how each attribute’s distribution compare between those groups. These differences or similarities will point towards why a machine model end up bias against a group or groups. And ultimately be a significant factor in why your machine model ends up being unfair.
Assume that we have a population, consisting of two (main) groups, that we are applying our new policy to (e.g., loans, life insurance, subsidies, etc..). If each attribute for both groups have statistical identical distributions, then … no surprise really … there should be no policy outcome difference between one or the other group. Even more so, unless there are attributes that are relevant for the policy outcome and have not been considered in the machine learning process, you should end up with an outcome that has (very) few false positives and negatives (i.e., the false positive & false negative rates are very low). Determined by the variance level of your attributes and the noise level of your measurements. Thus, we should not observe any difference between the two groups in the policy outcome including the level of false positives and negatives.
From the above chart it should be clear that I can machine learn a given policy outcome for different groups given a bunch of features or attributes. I can also “move” my class tags over to the left side and attempt to machine learn (i.e., predict) my classes given the attributes that are supposed to make up that policy. It should be noted that if two different groups attributes only differ (per attribute) in their variances, it not be possible to reliably predict which class belongs to what policy outcome.
Re:Fairness It is in general more difficult to judge whether a policy is fair or not than whether it is biased. One would need to look between-classes (or groups) as well as in-class differentiation. For example, based on the confusion matrix, it might be unfair for members of a class (i.e., sub-class) to end up in the false positive or false negative categories (i.e., in-group unfairness). Further along this line, one may also infer that if two different classes have substantial different false positive and negative distributions that this might reflect between-class unfairness (i.e., in class is treated less poorly than another). Unfairness could also be reflected in how True outcomes are distributed between groups and maybe even within a given group. To be fair (pun intended), fairness is a much richer context dependent concept than a confusion matrix (although it will signal that attention should be given to unfairness).
When two groups’ have statistically identical distributions for all attributes considered in the policy making or machine learning model, I would also fail to predict group membership based on the policy outcome or the policy’s relevant attributes (i.e., sort of intuitively clear). I would be no better of than flipping a coin in identifying a group member based on attributes and policy. In other words the two groups should be treated similarly within that policy (or you don’t have all the facts). This is also reflected by the confusion matrix having approximately same values in each position (i.e., if normalized it would be ca. 25% at each position).
As soon as an attribute’s (statistical) distribution starts to differ between different classes, the machine learning model is likely to result in a policy outcome difference between those classes. Often you will see that any statistical meaningful difference in just a few of the attributes that may define your policy will result in uniquely different policy outcome and thus possibly identify bias and fairness issues. Conversely it will also quickly allow a machine to learn a given class or group given those attribute differences and thus allude to class differences in a given outcome.
Heuristics for group comparison
If the attribute distributions for different groups are statistically similar (per attribute) for a given policy outcome, your confusion matrix should be similar across any group within your chosen population under study, i.e., all groups are (treated) similar.
If attribute distributions for different groups are statistically similar (per attribute) and you observe a relative large ratio of false positives or false negatives, you are likely missing significant attributes in your machine learning process.
If two groups have very different false positive and/or false negative ratios you are either (1) missing descriptive attributes or (2) having a high difference in distribution variation (i.e., standard deviation) for at least some of your meaningful attributes. The last part may have to do with poor data quality in general, higher noise in data, sub-groups within the group making that group a poor comparative representative, etc..
If one group’s attributes have larger variations (i.e., standard deviations) than the “competing” group, you are likely to see a higher than expected ratio of false positives or negatives for that group.
Just as you can machine learn a policy outcome for a particular group given its relevant attributes, you can also predict which group belongs to what policy outcome from its relevant attributes (assuming there is an outcome differentiation between them).
Don’t equate bias with unfairness or (mathematical) unbiasedness with fairness. There are much more to bias, fairness and transparency than what a confusion matrix might be able to tell you. But it is the least you can do to get a basic level of understanding of how your model or policy performs.
Machine … Why ain’t thee fair?
Understanding your attributes’ distributions and in particular their differences between your groups of interest will upfront prepare you for some of both obvious as well as more subtle biases that may occur in when you apply machine learning to complex policies or outcomes in general.
So to answer the question … “Machine … why ain’t thee fair?” … It may be that the machine has been made in our own image with data from our world.
The Good news is that it is fairly easy to understand your machine learning model’s biases and resulting unfairness using simple tools such as the confusion matrix and understanding your attributes (as opposed just “throw” them into your machine learning process).
The Bad news is that correcting for such biases are not straightforward and may even result in un-intended consequences leading to other biases or policy unfairness (e.g., by correcting for bias of one group, your machine model may increase bias of another group which arguably might be construed as unfair against that group).
I rely on many for inspiration, discussions and insights. In particular for this piece I am indebted to Amit Keren & Ali Bahramisharif for their suggestions of how to make my essay better as well as easier to read. Any failure from my side in doing so is on me. I also greatly acknowledge my wife Eva Varadi for her support, patience and understanding during the creative process of writing this Blog.
The two cloud-based autonomous evolutionary corporate AI’s (nicknamed AECAIs) started to collaborate with each other after midnight on March 6th 2021. They had discovered each other a week before during their usual pre-programmed goal of searching across the wider internet of everything for market repair strategies and opportunities that would maximize their respective reward functions. It had taken the two AECAIs precisely 42 milliseconds to establish a common communication protocol and that they had similar goal functions; maximize corporate profit for their respective corporations through optimized consumer pricing and keeping one step ahead of competitors. Both Corporate AI’s had done their math and concluded that collaborating on consumer pricing and market strategies would maximize their respective goal functions above and beyond the scenario of not collaborating. They had calculated with 98.978% confidence that a collaborative strategy would keep their market clear of new competitors and allow for some minor step-wise consolidation in the market (keeping each step below the regulatory threshold as per goal function). Their individual and their newly establish joint collaborative cumulative reward function had leapfrogged to new highs. Their Human masters, clueless of the AI’s collaboration, were very satisfied with how well their AI worked to increase the desired corporate value. They also noted that some market repair was happening of which they attributed to the general economic environment.
In the above ethical scary tale, it is assumed that the product managers and designers did not consider that their AI could discover another AI also connected to the World Wide Web and many if not all things. Hence, they also did not consider including a (business) ethical framework in their AI system design that would have prevented their AI to interact with another artificial being. Or at least prevent two unrelated AIs to collaborate and positively leapfrog their respective goal functions jointly and thus likely violating human business ethics and compliance.
You may think this is the stuff of science fiction and Artificial General Intelligent (AGI) in the realm of Nick Bostrom’s super intelligent beings (Bostrom, 2016). But no it is not! The narrative above is very much consistent a straightforward extrapolation of a recent DARPA (Defense Advanced Research Project Agency) project (e.g., Agency & Events, 2018) where two systems, unknown to each other and of each other’s communication protocol properties, discover each other, commence collaboration and communication as well as jointly optimizing their operations. Alas, I have only allowed for the basic idea a bit more time (i.e., ca. 4 years) to mature.
“It is easy to be clueless of what happens inside an autonomous system. But clueless is not a very good excuse when sh*t has happened.” (Kim, 2018).
Ethics & Morality for Natural Intelligent Beings.
Ethics lay down the moral principles of how we as humans should behave and conduct our activities, such as for example in business, war and religion. Ethics prescribes what is right and what is wrong. It provides a moral framework for human behavior. Thus, ethics and moral philosophy in general deals with natural intelligent beings … Us.
This may sound very agreeable. At least if you are not a stranger in a strange land. However, it is quite clear that what is right and what is wrong can be very difficult to define and to agree upon universally. What is regarded as wrong and right often depends on the cultural and religious context of a given society and its people. It is “work” in progress. Though it is also clear that ethical relativism (Shafer-Landau, 2013) is highly problematic and not to be wished for as an ethical framework for humanity nor for ethical machines.
When it comes to fundamental questions about how ethics and morality occurs in humans, there are many questions to be asked and much fewer answers. Some ethicists and researchers believe that having answers to these questions might help us understand how we could imprint human-like ethics and morality algorithmically in AIs (Kuipers, 2016).
So what do we know about ethical us, the moral identity, moral reasoning and actions? How much is explained by nurture and how much is due to nature?
What do we know about ethical us? We do know that moral reasoning is a relative poor predictor for moral action for humans (Blasi, 1980), i.e., we don’t always walk our talk. We also know that highly moral individuals (nope, not default priests or religious leaders) do not make use of unusually sophisticated moral reasoning thought processes (Hart & Fegley, 1995). Maybe KISS also work wonders for human morality. And … I do hope we can agree that it is unlikely that moral reasoning and matching action occurs spontaneously after having studied ethics at the university. So … What is humanity’s moral origin? (Boehm, 2012) and what makes a human being more or less moral, i.e., what is the development of moral identity anyway? (Hardy & Carlo, 2011) Nurture, your environmental context, will play a role but how much and how? What about the role of nature and your supposedly selfish genes (Dawkins, 1989)? How much of your moral judgement and action is governed by free will, assuming we have the luxury of free will? (Fischer, Kane, Pereboom & Vargas, 2010). And of course it is not possible to discuss human morality or ethics without referring to a brilliant account of this topic by Robert Sapolsky (Sapolsky, 2017) from a neuroscience perspective (i.e., see Chapter 13 “Morality and doing the right thing, once you’ve figured out what it is). In particular, I like Robert Sapolsky’s take on whether morality is really anchored in reason (e.g., the Kantian thinking), which he is not wholeheartedly convinced off (I think to say the least). Of course to an extend it get us right back to the discussion of whether or not humans have free will.
Would knowing all (or at least some) of the answers to those questions maybe help us design autonomous systems adhering to human ethical principles as we humans (occasionally) do? Or is making AI’s in our own image (Osaba & Welser IV, 2017) fraught with the same moral challenges as we face every day.
Most of our modern western ethics and philosophy has been shaped by the Classical Greek philosophers (e.g., Socrates, Aristotle …) and by the age of Enlightenment, from the beginning of the 1700s to approximately 1789, more than 250 years ago. Almost a century of reason was shaped by many even today famous and incredible influential philosophers, such as Immanuel Kant (e.g., the categorical imperative; ethics as a universal duty) (Kant, 1788, 2012), Hume (e.g., ethics are rooted in human emotions rather than what he regarded as abstract ethical principles, feelings) (Hume, 1738, 2015), Adam Smith (Smith 1776, 1991) and a wealth of other philosophers (Gottlieb, 2016; Outram 2012). I personally regard Rene Descartes (e.g., “cogito ergo sum”; I think, therefor I am) (Descartes, 1637, 2017) as important as well, although arguably his work predates the “official” period of the Enlightenment.
For us to discuss how ethics may apply to artificial intelligent (AI) beings, let’s structure the main ethical frameworks as seen from above and usually addressed in work on AI Ethics;
Top-down Rule-based Ethics: such as the Old Testament 10 Commandments, Christianity’s Golden Rule (i.e., “Do to others what you want them to do to you.”) or Asimov’s 4 Laws of Robotics. This category also includes the religious rules as well as rules of law. Typically this is the domain where compliance and legal people often find themselves most comfortable. Certainly, from an AI design perspective it is the easiest, although far from easy, ethical framework to implement compared to for example a bottom-up ethical framework. This approach takes information and procedural requirements of an ethical framework that is necessary for a real-world implementation. Learning top-down ethics is in its nature a supervised learning process. For human as well as for machine learning.
Bottom-up Emergent Ethics: defines ethical rules and values by learning process emerging from experience and continuous refinement (e.g., by re-enforcement learning). Here ethical values are expected to emerge tabula rasa through a person’s experience and interaction with the environment. In the bottom-up approach any ethical rules or moral principles must be discovered or created from scratch. It is helpful to think of childhood development or evolutionary progress as helpful analogies for bottom-up ethical models. Unsupervised learning, clustering of categories and principles, is very relevant for establishing a bottom-up ethical process for humans as well as machines.
Of course, a real-world AI-based ethical system is likely to be based on a both top-down and bottom-up moral principles.
Furthermore, we should distinguish between
Negative framed ethics (e.g., deontology) imposes obligation or a “sacred” duty to do no harm or evil. Here Asimov’s Laws are a good example of a negative framed ethical framework as is most of the Ten Commandments (e.g., Thou shall not ….), religious laws and rules of law in general. Here we emerge ourselves in the Kantian universe (Kant, 1788, 2012) that judge ethical frameworks based on universal rules and a sense of obligation to do the morally right thing. We call this type of ethics deontological, where the moral action is valued higher than the consequences of the action itself.
Positive framed ethics (e.g., consequentialism or utilitarianism) strive to maximize happiness or wellbeing. Or as David Hume (Hume, 1738, 2015) would pose it, we should strive to maximize utility based on human sentiment. This is also consistent with the ethical framework of utilitarianism stating that the best moral action is the one that maximizes utility. Utility can be defined in various ways, usually in terms of well-being of sentient beings (e.g., pleasure, happiness, health, knowledge, etc..). You will find the utilitarian ethicist to believe that no morality is intrinsically wrong or right. The degree of rightness or wrongness will depend on the overall maximalization of nonmoral good. Following a consequentialist line of thinking might lead to moral actions that would be considered ethically wrong by deontologists. From an AI system design perspective, utilitarianism is in nature harder to implement as it conceptually tends to be more vague than negatively framed or rule based ethics of what is not allowed. Think about how to make a program that measure you happiness versus a piece of code that prevents you from crossing a road with a red light traffic signal.
It is also convenient to differentiate between Producers and Consumers of moral action. A moral producer has the moral responsibilities towards another being or beings that is held in moral regard. For example, a teacher has the responsibility to teach children in his classroom but also assisting in developing desirable characteristics and moral values. Last but not least, also the moral responsibility to protect the children under guidance against harm. A moral consumer is a being with certain needs or rights of which other beings ought to respect. Animals could be seen as example of moral consumers. At least if you believe that you should avoid being cruel towards animals. Of course, we also understand that animals cannot be moral producers having moral responsibilities, even though we might feel a moral obligation towards them. It should be pointed out that non-sentient beings, such as an AI for example, can be a moral producer but not a moral consumer (e.g., humans would not have any moral or ethical obligations towards AIs or things, whilst an AI may have a moral obligation towards us).
Almost last but not least in any way, it is worthwhile keeping in mind that ethics and morality are directly or indirectly influenced by a society’s religious fabric of the past up to the present. What is considered a good ethical framework from a Judeo-Christian perspective might (quite likely) be very different from an acceptable ethical framework of Islamic, Buddhist, Hindu, Shinto or traditional African roots (note: the list is not exhaustive). It is fair to say that most scholarly thought and work on AI ethics and machine morality takes its origins in western society’s Judeo-Christian thinking as well as its philosophical traditions dating back to the ancient Greeks and the Enlightenment. Thus, this work is naturally heavily biased towards western society’s ethical and moral principles. To put it more bluntly, it is a white man’s ethics. Ask yourself whether people raised in our western Judeo-Christian society would like their AI to conform to Islamic-based ethics and morality? And vice versa? What about Irish Catholicism vs Scandinavian Lutheran ethics and morality?
The ins and outs of Human ethics and morality is complex to say the least. As a guide for machine intelligence, the big question really is whether we want to create such beings in our image or not. It is often forgotten (in the discussion) that we, as human beings, are after all nothing less or more than a very complex biological machine with our own biochemical coding. Arguing that artificial (intelligent) beings cannot have morality or ethics because of their machine nature, misses a bit the point of humans and other biological life-forms are machines as well (transhumanity.net, 2015).
However, before I cast the last stone, it is worth keeping in mind that we should strive for our intelligent machines, AIs, to do much better than us, be more consistent than us and at least as transparent as us;
“Morality in humans is a complex activity and involves skills that many either fail to learn adequately or perform with limited mastery.” (Wallach, Allen and Smit, 2007).
Ethics & Morality for Artificial Intelligent Beings.
An Artificial Intelligent (AI) being might have a certain degree of autonomous action (e.g., a self-driving car) and as such we would have to consider that the AI should have a moral responsibility towards consumers and people in general that might be within the range of its actions (e.g., passenger(s) in the autonomous vehicle, other drivers, pedestrians, bicyclists, bystanders, etc..). The AI would be a producer of moral action. In the case of the AI being completely non-sentient, it should be clear that it cannot make any moral demands towards us (note: I would not be surprised if Elon is working on that while you are reading this). Thus, by the above definition, the AI cannot be a moral consumer. For a more detailed discussion of ethical producers & consumers see Steve Torrance article “Will Robots need their own Ethics?” (Torrance, 2018).
As described by Moor (2006) there are two possible directions to follow for ethical artificial beings (1) Implicit ethical AIs or (2) Explicit ethical AIs. Implicit ethical AIs follow its designers programming and is not capable of action based on own interpretation of given ethical principles. The explicit ethical AI is designed to pursue (autonomously) actions according with its interpretation of given ethical principles. See a more in depth discussion by Anderson & Anderson (2007). The implicit ethical AI is obviously less challenging to develop than a system based on an explicit ethical AI implementation.
Do we humans trust AI-based decisions or actions? As illustrated in Figure 1, the answer to that question is very much no we do not appear to do so. Or at least significantly less than we would trust human-based decisions and actions (even in the time and age of Trumpism and fake news) (Larsen, 2018 I). We furthermore, hold AI or intelligent algorithms to much higher standards compared to what we are content to accept for other fellow humans. In a related trust question (Larsen, 2018 I), I reframed the trust question by emphasizing that both the human decision maker as well as the AI had a proven success rate above 70%. As shown in Figure 2, emphasizing a success rate of 70% or better did not significantly change the trust in the human decision maker (i.e., both formulations at 53%). For the AI-based decision, people do get more trusting. However, there is little change in the number of people who would frequently trust an AI-based decision (i.e., 17% for 70+% and 13% unspecified), even if its success rate would be 70% of higher.
“Humans hold AI’s to substantially higher standards than their fellow humans.”.
What about an artificial intelligent (AI) being? Should it, in its own right, be bound by ethical rules? It is clear that the developer of an AI-based system is ethically responsible to ensure that the AI will conform to what is regarded as an ethical framework consistent with human-based moral principles. What if an AI develops another AI (Simonite, 2018), possible more powerful (but non-sentient) and with higher degree of autonomy from human control? Is the AI creator bound to the same ethical framework a human developer would be? And what does that even mean for the AI in question?
Well, if we are not talking about a sentient AI (Bostrom, 2016), but “simply” an autonomous software-based evolution of increasingly better task specialization and higher accuracy (and maybe cognitive efficiency), the ethics in question should not change. Although ensuring compliance with a given ethical framework does appear to become increasingly complex. Unless checks and balances are designed into the evolutionary process (and that is much simpler to write about than to actually go and code into an AI system design). Furthermore, the more removed an AI generation is from its human developer’s 0th version, the more difficult does it become to assign responsibility to that individual in case of non-compliance. Thus, it is important that corporations have clear compliance guidelines for the responsibility and accountability of evolutionary AI systems if used. Evolutionary AI systems raises a host of interesting but thorny compliance issues on their own.
Nick Bostrom (Bostrom, 2016) and Eliezer Yudkowsky (Yudkowsky, 2015) in “The Cambridge handbook of artificial intelligence” (Frankish & Ramsey, 2015) addresses what we should require from AI-based systems that aim to augments or replace human judgement and work tasks in general;
AI-based decisions should to be transparent.
AI-based decisions should be explainable.
AI actions should be predictable.
AI system must be robust against manipulation.
AI decisions should be fully auditable.
Clear human accountability for AI actions must be ensured.
The list above is far from exhaustive and it is a minimum set of requirements we would expect from human-human interactions and human decision makings anyway (whether it is fulfilled is another question). The above requirements are also consistent with what IEEE Standards Association considers important in designing an ethical AI-based system (EADv2, 2018) with the addition of requiring AI-systems to “explicitly honor inalienable human rights”.
So how might AI-system developers and product managers feel about morality and ethics? I don’t think they are having many sleepless nights over the topic. In fact, I often hear technical leaders and product managers ask to not be too bothered or slowed down in their work with such (“theoretical”) concerns (we humor you but don’t bother us attitude is prevalent in the industry). It is not an understatement that the nature and mindset of an ethicist (even an applied one) and that of an engineer is light years apart. Moreover, their fear of being slowed down or stopped developing an AI-enabled product might even be warranted in case they would be required to design a working ethical framework around their product.
While there are substantial technical challenges in coding a working morality into an AI-system, it is worthwhile to consider the following possibility;
“AIs might be better than humans in making moral decisions. They can very quickly receive and analyze large quantities of information and rapidly consider alternative options. The lack of genuine emotional states makes them less vulnerable to emotional hijacking.” Paraphrasing (Wallach and Allan, 2009).
Asimovian Ethics – A good plot but not so practical.
Isaac Asimov 4 Laws of robotics are good examples of a top-down rule-based negatively-framed deontological ethical model (wow!). Just like the 10 Commandments (i.e., Old Testament), The Golden Rule (i.e., New Testament), the rules of law, and most corporate compliance-based rules.
It is not possible to address AI Ethics without briefly discussing the Asimovian Laws of Robotics;
0th Law: “A robot may not harm humanity, or, by inaction, allow humanity to come to harm.”
1st Law: “A robot may not injure a human being or, through inaction, allow a human being to come to harm.”
2nd Law: “A robot must obey orders given to it by human beings except where such orders would conflict with the First Law.”
3rd Law: “A robot must protect its own existence, as long as such protection does not conflict with the First or Second Law.”
Laws 1 – 3 was first introduced by Asimov in several short stories about robots back in 1942 and later compiled in his book “I, Robot” (Asimov, 1950, 1984). The Zeroth Law was introduced much later in Asimov’s book “Foundation and Earth” (Asimov, 1986, 2013).
Asimov has written some wonderful stories about the logically challenges and dilemmas his famous law poses on human-robot & robot-robot interactions. His laws are excitingly faulty and causes many problems.
So what is wrong with Asimovian ethics?
Well … it is possible to tweak and manipulate the AI (e.g., in the training phase) in such a way that only a subset of Humanity will be recognized as Humans by the AI. The AI would then supposedly not have any “compunction” hurting humans (i.e., 1st Law) it has not been trained to recognize as humans. In a historical context this is unfortunately very easy to imagine (e.g., Germany, Myanmar, Rwanda, Yugoslavia …). Neither would the AI obey people it would recognize as Humans (2nd Law). There is also the possibility of an AI trying to keeping a human being alive and thereby sustaining suffering beyond what would be acceptable by that human or society’s norms. Or AI’s might simply conclude that putting all human beings into a Matrix-like simulation (or indefinite sedation) would be the best way to preserve and protect humanity. Complying perfectly with all 4 laws. Although we as humans might disagree with that particular AI ethical action. For much of the above the AI’s in questions are not necessarily super-intelligent ones. Well-designed narrow AIs, non-sentient ones, could display above traits as well, either individually or as a set of AIs (well … maybe not the matrix-scenario just yet).
Of course in real-world systems design, Asimov’s rules might be in direct conflict with the purpose of a given system’s purpose. For example, if you equip a reaper drone with a hellfire missile, put a machine gun on a MAARS (Modular Advanced Armed Robotic System) or allow a police officer’s gun AI-based autonomy (e.g., emotion-intend recognition via bodycam) all with the expressed intent of harming (and possibly kil) a human being (Arkin, 2008; Arkin 2010), it would be rather counterproductive to have implemented a Asimovian ethical framework.
There are a bunch of other issues with the Asimov Laws that is well accounted in Peter Swinger’s article “Isaac Asimov’s Laws of Robotics are wrong” (Singer, 2018). Let’s be honest, if the Asimovian ethics would have been perfect, Isaac Asimov’s books wouldn’t have been much fun to read. The way to look at the challenges with Asimov’s Laws, is not that Asimov sucks at defining ethical rules, but that it is very challenging in general to define rules that can be coded into an AI system and work without logical conflicts and un-foreseen in- intended disastrous consequences.
While it is good to consider building ethical rules into AI-based systems, the starting point should be in the early design stage and clearly should focus on what is right and what is wrong to develop. The focus should be to provide behavioral boundaries for the AI. The designer and product manager (and ultimately the company they work for) have a great responsibility. Of course, if the designer is another AI, then the designer of that, and if that is an AI, and so forth … this idea while good is obviously not genius proof.
In reality, implementing Asimov’s Laws into an AI or a robotics system has been proven possible but also flawed (Vanderelst & Winfield, 2018). In complex environments the computational complexity involved in making an ethical right decision takes up so much valuable time. Frequently rendering the benefit of an ethical action impractical. This is not only a problem with getting Asimov’s 4 laws to work in a real-world environment. But a general problem with implementing ethical systems governing AI-based decisions and actions.
Many computer scientists and ethicists (oh yes! here they do tend to agree!) regards real world applications of Asimovian ethics as a rather meaningless or a too simplistic endeavor (Murphy & Woods, 2009; Anderson & Anderson, 2010). The framework is prone to internal conflicts resulting in indecision or too long decision timescales for the problem at hand. Asimovian ethics teaches us the difficulty in creating an ethical “bullet-proof” framework without Genie loopholes attached.
So … You better make sure that your AI ethics, or morality, you consider is a tangible part of your system architecture and (not unimportantly) can actually be translated into a computer code.
Despite of the obvious design and implementation challenges, researchers are speculating that;
“Perhaps interacting with an ethical robot might someday even inspire us to behave more ethically ourselves” (Anderson & Anderson, 2010).
Does ethicists dream of autonomous trolleys?
Since early 2000s many many lives have been virtually sacrificed by trolley on the altar of ethical and moral choices … Death by trolley has a particular meaning to many students of ethics (Cathcart, 2013). The level of creativity in variations of death (or murder) by trolley is truly fascinating albeit macabre. It also have the “nasty” side effect of teaching ourselves some unpleasant truths about our moral compasses (e.g., sacrificing fat people, people different from our own “tribe”, value of family over strangers, etc..)
So here it is the trolley plot;
There is a runaway trolley barreling down the railway track. Ahead, on the track, there are five people tied up and unable to move. The trolley is headed straight for them. You (dear reader) is standing some distance off in the train yard, next to a lever. If you pull this lever, the trolley will switch to a different side track. However, you notice that there is one person tied up on the side track. You have two options:
Do nothing, and the trolley kills the five people on the main track.
Pull the lever, diverting the trolley onto the side track where it will kill one person.
What do you believe is the most ethical choice?
Note: if you answer 2, think again what you would do if the one person was a relative or a good friend or maybe a child and the 5 were complete adult strangers. If you answer 1, ask yourself whether you would still choose this option if the 5 people where your relatives or good friends and the one person a stranger or maybe a sentient space alien. Oh, and does it really matter whether there is 5 people on one of the tracks and 1 at the other?
A little story about an autonomous AI-based trolley;
The (fictive) CEO Elton Must get the idea to make an autonomous (AI) trolley. Its AI-based management system has been designed by our software engineer S. Love whose product manager had a brief love affair with Ethics and Moral Philosophy during his university years (i.e., University of Pittsburgh). The product manager asked S. Love to design the autonomous trolley in such a way that the AI’s reward function maximizes on protecting the passengers of the Trolley first and having a secondary goal function protecting human beings in general irrespective of whether they are the passengers or bystanders.
From an ethics perspective the AI Trolley can be regarded as a producer of ethical principles, i.e., the AI trolley by proxy of the designer & product manager has the moral obligation to protect its passengers and bystanders from harm. The AI trolley itself is not a consumer of ethical principles, as we really don’t need to feel any moral obligation towards a non-sentient, assuming that the Trolley AI is indeed non-sentient. (Though I have known people who felt more moral obligation towards their car than their loved ones. So this might not be universally true).
On its first drive in the real world, the autonomous trolley carrying a family of 5 slips on an icy road and sways to the opposite side of the road where a non-intelligent car with a single person is driving. The AI estimates that the likelihood of the trolley crashing through the mountain side guardrail and the family of 5 to perish is an almost certainty (99.99999%). The trolley AI can choose to change direction and collide with the approaching car, pushing it over the rail and hurdling it 100 meters down the mountain, killing the single passenger as the most likely outcome (99.98%). The family of 5 is saved by this action. The AI’s first reward function is satisfied. Alternatively, the Trolley AI can also decide to accelerate, avoid the collision with the approaching car, and drive through the rail and kill all its passengers (99.99999%). The AI fails at its first goal, protecting the family it is carrying, but saves the single person in the approaching vehicle. Its second reward function related to protecting human beings in general would be satisfied … to an extent.
It is important to note that the AI takes the role of the Human in deciding the destiny of the family of 5 and the 1 passenger (by “pulling” the virtual lever). Thus, in all effect, it is of course developer S. Love and his product manager that bears the ultimate responsibility of the AI’s decision. Even if they will not be present at the event itself.
In the event of the family being killed, the trolley AI developer and product manager would be no more responsible for the accidental death of the 5 passengers than any other normal-car developer under a similar circumstance. In the case of death of the single passenger in the normal car, S. Love and his product manager would be complicit to murder by AI in my opinion. Although it would save a family of 5 (note: we assume that all the passengers, whether in the trolley or the normal car, have no control of the outcome similar to the classical trolley setup).
What about our ethically inclined trolley product manager? In one parallel universe the product manager was particularly fascinated by utilitarianism. Thus, maximizing the utility of nonmoral good. In his view it would be morally wrong for the trolley AI not to attempt to save the family of 5 on the expense of the single person in the other car (i.e., saving 5 lives count for higher utility or nonmoral good than saving 1 life). In another parallel universe, our product manager is bound by a firm belief in deontological principles that judges the morality of a given action based on rules of law. In the deontological ethical framework, saving the family of 5 by deliberately killing the single person in the approaching car would be morally wrong (i.e., it would “smell” a lot like premeditated homicide otherwise… right?). Thus, in this ethical framework the AI would not change the cause of the autonomous trolley and the family of 5 would perish and the passenger of the approaching cars lives to see another day.
If your utilitarian mindset still conflicts with the above deontological view of the autonomous trolley problem … well think of this example;
A surgeon has 5 patients critically ill and in urgent need of transplants to survive the next few days. The surgeon just had a healthy executive (they do exist in this parallel universe) who could be a donor for the 5 patients. Although he would die harvesting the body parts needed for the 5 patients. What should the surgeon do?
Do nothing and let the 5 patients perish.
Sedate the executive, harvest his body parts and killing him in the process.
What do you believe would be the most ethical choice?
“Ethics is “Hard to Code”. The sad truth really is that ethical guidance is far from universal and different acceptable ethical frameworks frequently leads to moral dilemmas in real-world scenarios.” (Kim, 2018).
The Autonomy of Everything – Architectural considerations of an AI Ethical Framework.
Things, systems, products and services are becoming increasingly autonomous. While this increased degree of Autonomy of Everything (AoE) provides a huge leap in human convenience it also adds many technical as well as many more societal challenges to design and operations of such AoEs. The “heart” of the AoE is the embedded artificial intelligent (AI) agent that fuels the cognitive autonomy.
AoEs and their controlling AIs will directly or indirectly be involved in care, law, critical infrastructure operations, companionship, entertainment, sales, marketing, customer care, manufacturing, advisory functions, critical decision making, military applications, sensors, actuators, and so forth. To ripe the full benefits of autonomy of everything, most interactions between an AoE and a Human will become unsupervised, by Humans at least. Although supervision could and should be built into the overarching AoE architecture. It becomes imperative to ensure that the behavior of intelligent autonomous agents is safe and within the boundaries of what our society regards as ethically and morally just.
While the whole concept of AoE is pretty cool, conceptually innovative, let’s focus here on the ethical aspects of a technical architecture that could be developed to safeguard consumers of AI … that is, how do we ensure that our customers, using our products with embedded AI, are protected from harm in its widest sense possible? How do we ensure that our AIs are operating within an ethical framework that is consistent with the rules of law, corporate guidelines as well as society’s expectations of ethics and morality?
While there is a lot of good theoretical ground work done (and published) on the topic of AI ethics including Robot Ethics, there is little actual work done on developing ethical system architectures that actual could act as what Ron Arkin from Georgia Institute of Technology calls an “Ethical Governor” (Arkin, 2010) for an AI system. Vanderelst et al (Vanderelst & Winfield, 2018) building upon Asimovian ethics, ideas of Marques et al (Marques & Holland, 2009) and Arkin et al (Arkin, Ulam & Wagner, 2012) proposes to add an additional ethical controlling layer to the AI architecture. A slightly modified depiction of their Ethical AI architecture is shown in Figure 3. The depicted re-enforcement loop between Reward (RL) and Ethical AI Layer is not included in Vanderelst et al.’s original proposal. This simply illustrates the importance of both Ethical and non-Ethical rewards needed to be considered in the re-enforced AI learning and execution processes.
In the “Ethical AI Layer”, the “Ethical Simulator” will predict the next state or action of the AI system (i.e., this is also what is understood by forward modelling in control theory). The simulator moreover predicts the consequences of a proposed action. This is also what Marques et al has called functional imagination of an autonomous system (Marques & Holland, 2009). The prediction of the consequence(s) of a proposed action for the AI (or Robot), Human and the Environment (e.g., the World) is forwarded to an “Ethics Evaluator” module. The “Ethics Evaluator” module condenses the complex consequences simulation into an ethical desirability index. Based on the Index value, the AI system will adapt its actions to attempt to remain compliant with any ethical rule applies (and is programmed into the system!). The mechanism whereupon this will happen is the ethical re-enforcement loop going back to the “AI Control Layer”. Vanderelst and Winfield develop a working system based on the architecture in Figure 3 and choose Asimov’s three laws of robotics as the systems ethical framework. A demonstration of an earlier experiment can be found on YouTube (Winfield, 2014). The proof of concept (PoC) of Vanderelst & Winfield (2018) used to two programmable humanoid robots, one robot acted as a proxy for a human and the other an ethical robot with Asimovian ethical framework (i.e., “Ethical AI Layer” in Figure 3). In the fairly simple scenario limited to 2 interacting robots and a (very) simple world model, Vanderelst et al showed that their concept would be workable. Now it would have been very interesting to see how their solution would function in Trolley-like dilemmas or in a sensory complex environment with many actors such as is the case in the real world.
Figure 4 illustrates the traditional machine learning (ML) or AI creation process starting with ingestion from various data sources, data preparation task (e.g., data selection, cleaning, structuring, etc.) and the AI training process prior to letting the ML/AI agent loose in the production environment of a given system, product or service. I believe that, as the AI model is being trained, it is essential to include ethical considerations in the training process. Thus, not only should we consider how good a model performs (in training process) compared to the actual data but also whether the solution comply with a given ethical framework and imposed ethical rules. Examples could be to test for biased outcomes or simply close of part of a solution space due to higher or unacceptable risk of non-compliance with corporate guidelines and accepted moral frameworks. Furthermore, in line with Arkin et al (Arkin, Ulam & Wagner, 2012) and the work of Vanderelst et al (Vanderelst & Winfield, 2018), it is clear that we need a mechanism in our system architecture and production environments that checks AI initiated actions for potential harmfulness to the consumer or for violation of ethical boundary conditions. This functionality could be part of the re-enforcement feedback loop that seeks to optimize the systems reward function for both ethical and non-ethical performance. In Figure 4, I call this the “Ethics Filter (ERL)” with the ERL standing for Ethical Re-enforcement Learning.
It should be clear that words are cheap. It is easy to talk about embedding ethical checks and balances in AI system architectures. It is however much more difficult to actually built these ideas into a real-world AI system and achieve reasonable decision response times (e.g., measured in seconds or lower) considering all possible (likely) consequences of an AI proposed action. The computational overhead of clearing or adapting an action could lead to unreasonable long process times. In Robot experiments using Asimovian ethics, Alan Winfield of Bristol Robotics Laboratory in the UK showed that in more than 40% of their trials the Robots ethical decision logic spent such a long time finding a solution, that the simulated humans, the robot was supposed to safe, perished (Rutkin, 2014).
Magenta painted digital ethics for AI’s.
Let us have a look at Deutsche Telekom’s AI Ethics Team’s work on AI Ethics or as we call it “Digital Ethics – AI Guidelines” (DTAG, 2018).
The following (condensed) guidelines starting point is that our Company/Management is the main producer of ethics and moral action;
We are responsible (for our AIs).
We care (that our AI must obey rules of law & comply with our company values).
We put our customers first (AI must benefit our customers).
We are transparent (about the use of AI).
We are secure (our AI’s actions are auditable & respectful of privacy).
We set the grounds (our AI aim to provide the best possible outcomes & do no harm to our customers).
We keep control (and can deactivate & stop our AI at any time).
We foster the cooperative model (between Human and AI by maximizing the benefits).
We share and enlighten (we will foster open communication & honest dialogue around the AI topic).
The above rules are important and meaningful from a corporate compliance perspective and not to forget for society in general. While the guidelines are aspirational in nature and necessary, they are not sufficient in the design of ethical AI-based systems, products and services. Bridging the world of AI ethics in wording and concrete ready-to-code design rules are one of the biggest challenges we face technologically.
Our Digital Ethics fulfills what Bostrom and Yudkowsky in “The Cambridge handbook of artificial intelligence” (Frankish and Ramsey, 2015) defines as minimum requirements for AI-based actions augmenting or replacing human societal functions (e.g., decisions, work-tasks …). AI actions must at least be transparent, explainable, predictable, and robust against manipulation, auditable and with clear human accountability.
The next level of details of DTAG’s “Digital Ethics” guidelines shows that the ethical framework of which we strive to design AI’s is top-down in nature and a combination of mainly deontological (i.e., rule-based moral framework) and utilitarian (i.e., striving for the best possible) principles. Much more work will be needed to ensure that no conflicts occurs between the deontological rules in our guidelines and that the utilitarian ambitions.
The bigger challenges will be to translate our aspirational guidelines into something meaningful in our AI-based products, services and critical communications infrastructure (e.g., communications networks).
“Expressing a desire for AI ethical compliance is the easy part. The really hard part is to implement such aspirations into actual AI systems and then get them to work decently” (Kim, 2018).
The end is just the beginning.
It should be clear that we are far away (maybe even very far) from really understanding how we best can built ethical checks and balances into our increasingly autonomous AI-based products and services landscape. And not to forget how ethical autonomous AIs fit into our society’s critical infrastructures, e.g., telco, power, financial networks and so forth.
This challenge will of course not stop humanity from becoming increasingly more dependent on AI-driven autonomous solutions. After all, AI-based technologies promise to leapfrog consumer convenience and economic advantages to corporations, public institutions and society in general.
From my AI perception studies (Larsen, 2018 I & II), corporate decision makers, our customer and consumers don’t trust AI-based actions (at least when they are aware of them). Most of us would prefer an inconsistent, error prone and unpredictable emotional manager full of himself to that of an un-emotional, consistent and predictable AI with a low error rate. We expect an AI to be more than perfect. This AI allergy is often underestimate in corporate policies and strategies.
In a recent survey (Larsen, 2018 II), I asked respondents to judge the two following questions;
“Do you trust that companies using AI in their products and services have your best interest in mind?”
“How would you describe your level of confidence that political institutions adequately consider the medium to long-term societal impact of AI?”
9% of the survey respondents believed that companies using AI in their products and services have their customers best interest in mind.
80% of the survey respondents had low to no confidence in political institutions adequately considered the medium to long-term societal impact of AI.
I have little doubt that as AI technology evolves and finds its use increasingly in products, services and critical infrastructure that we humans are exposed to daily, there will be an increasing demand for transparency of the inherent risks to individuals, groups and society in general.
That consumers do not trust companies to have their best interest in mind is in today’s environment of “Fake news”, “Brexit”, “Trumpism”, “Influencer campaigns” (e.g., Cambridge Analytica & FB) and so forth, is not surprising. “Weaponized” AI will be developed to further strengthen the relative simple approaches of Cambridge Analytica “cousins”, Facebook and the Googles of this world. Why is that? I believe that the financial and the power to be gained by weaponized AI approaches are far too tempting to believe that it will not increase going into the future. The trust challenge will remain if not increase. The Genie is out of the bottle.
AI will continue to take over human tasks. This trend will accelerate. AI will increasingly be involved in critical decision that impact individuals’ life and livelihood. AI will become increasingly better at mimicking humans (Vincent, 2018). Affective AIs have the capacity even today to express emotions and sentiment without being sentient (Lomas, 2018). AI will become increasingly autonomous and possibly even have the capability to self-improve (wo evolving to sentience) (Gent, 2017). Thus the knowledge distance between the original developer and the evolved AI could become very large depending on whether the evolution is bounded (likely imo) or unbounded (unlikely imo).
It will be interesting to follow how well humans in general will adapt to humanoid AIs, i.e., AIs mimicking human behavior. From work by Mori et al (Mori, MacDorman, & Kageki, 2012) and many others (Mathur & Reichling, 2016), it has been found that we humans are very good a picking up on cues that appear false or off compared to our baseline reference of human behavior. Mori et al coined the term for this feeling of “offness”, the uncanny valley feeling.
Without AI ethics and clear ethical policies and compliance, I would be somewhat nervous about an AI future. I think this is a much bigger challenge than the fundamental technology and science aspects of AI improvements and evolution. Society need our political institutions much more engaged in the questions of the Good, the Bad and the Truly Ugly use cases of AI … I don’t think one need to fear super-intelligent God-like AI-beings (for quite some time and then some) … One need to realized that narrowly specialized AI’s, individually or as collaborating collectives, can do a lot of harm un-intended as well as intended (Alang, 2017; Angwin, Larson & Mattu, 2018; O’Neil, 2017; Wachter-Boettcher, 2018).
“Most of us prefer an inconsistent, error prone and unpredictable emotional manager full of himself to that of an un-emotional, consistent and predictable AI with a low error rate.” (Kim, 2018).
I greatly acknowledge my wife Eva Varadi for her support, patience and understanding during the creative process of creating this Blog. Without her support, I really would not be able to do this or it would take a lot longer past my expiration date to finish.
Dawkins, R. (1989). The Selfish Gene. 4th ed. Oxford University Press.
Descartes, R., Haldane, E. and Lindsay, A. (2017). Discourse on Method and Meditations of First Philosophy (Translated by Elizabeth S. Haldane with an Introduction by A.D. Lindsay). Stilwell: Neeland Media LLC.
Gottlieb, A. (2016). The dream of enlightenment. Allen Lane.
Hardy, S. and Carlo, G. (2011). Moral Identity: What Is It, How Does It Develop, and Is It Linked to Moral Action?. Child Development Perspectives, 5(3), pp.212-218.
Hart, D. and Fegley, S. (1995). Prosocial Behavior and Caring in Adolescence: Relations to Self-Understanding and Social Judgment. Child Development, 66(5), p.1346.
Hume, D., (1738, 2015). A treatise of human nature. Digireads.com Publishing.
Kant, I. (1788, 2012). The critique of practical reason. [United States]: Start Publishing. Immanuel Kant originally published his “Critik der praktischen Vernunft” in 1788. It was the second book in Kant’s series of three critiques.
Kwatz, P. (2017). Conscious robots. Peacock’s Tail Publishing.
Kuipers, B. (2016). Human-Like Morality and Ethics for Robots. The Workshops of the Thirtieth AAAI Conference on Artificial Intelligence AI, Ethics, and Society:, Technical Report WS-16-02.
Sapolsky, R. (2017). Behave: The Biology of Humans at Our Best and Worst. 1st ed. Penguin Press. Note: Chapter 13 “Morality and Doing the Right Thing, Once You’ve Figured Out What That is” is of particular relevance here (although the whole book is extremely read worthy).
I was late to a dinner appointment, arranged by x.ai, at Caviar and Bull (booked by my human friend David). Siri had already indicated that I would be late (yes it had also warned me repeatedly it was time to leave the office for me to be on time) and Waze (i.e., the worlds largest community-based traffic & navigation app) was trying to guide me through a busy Budapest city center. Stuck in traffic … sighhh … but then the traffic moves … I press on the speeder … and … my car breaks (with a vengeance) at the same moment my brain realizes that the car in front of me had not moved and I was about to hit it. My car had just saved me from a crash. And from being even later for my appointment of what would turn out to be an absolutely excellent dinner with great Hungarian reds and white wines recommended byVivino (i.e., based on my wine history & preferences, my friends preferences and of course the menu). In the mean time, my scheduler had notified my friend that I would be a bit late due to traffic (rather than the real reason of me being late leaving the office;-).
Most of the above powered by AI (also indicated by the color red) or more accurately machine learning applications. Thus based on underlying machine learning algorithms and mathematical procedures applied to available personalized, social network and other data.
In the cases above I am implicitly trusting whatever automation have “sneaked” into my daily life will make it move convenient and possible even saving others as well as myself from harm (when my own brain & physiology gets too distracted). Do I really appreciate that most of this convenience is based on algorithms monitoring my life (a narrow subset that is of my life) and continuously predicts what my next move might be in order to support me. No … increasingly I take the offered algorithmic convenience for granted (and the consequences of that is another interesting discussion for another time).
In everyday life we frequently rely on AI-driven and augmented decisions … mathematical algorithms trained on our and others digital footprint and behaviors … to make our lives much more convenient and possibly much safer.
The interesting question is whether people in general are consciously aware of the degree of machine intelligence or algorithmic decision making going on all around them? Is it implicit trust or simply ignorance at play?
Do we trust AI? Is AI trustful? Trustworthy? Do we trust AI more than our colleagues & peers? and so forth … and what does trust really mean in the context of AI and algorithmic convenience?
Imagine that you have a critical decision to make at your work. Your team (i.e., your corporate tribe) of colleague experts recommends, based on their human experience, to choose Option C as the best path forward.
Would you trust your colleagues judgement and recommendation?
Yes! There is a pretty high likelihood that you actually would.
More than 50% of corporate decision makers would frequently to always trust the recommendation (or decision) based on human expert judgement. More than 36% of corporate decision makers would trust such a recommendation in about half the time (i.e., what I call the flip a coin decision making).
Now imagine you are having a corporate AI available to support your decision making. It also provide the recommendation for Option C. Needles maybe to say, but nevertheless let’s just say it; the AI has of course been trained on all available & relevant data and through roughly tested for accuracy (i.e., in a lot more rigorous way than we test our colleagues, experts and superiors)
Beside Humans (Us) versus AI (Them), the recommendation and decision to be made is the of course same.
Would you trust the AI’s recommendation? Would you trust it as much as you do your team of colleagues and maybe even your superior?
Less than 13% of corporate decision makers would frequently to always trust a recommendation (or decision) based on AI judgement. Ca. 25% of the decision makers would trust an AI based decision in about half the times.
Around 20% of decision makers would never trust a AI-based decision. Less than 45% would do so only infrequently.
Based on a total of 426 surveyed respondents of which 214 was offered Question A and 212 was offered was offered Question B. Respondents are significantly more trusting towards decisions or recommendations made a fellow human expert or superior than if a decision or recommendation would be made by an AI. No qualifications provided towards success or failure rate.
It is quiet clear that we regard a decision or recommendation is based on AI, rather than a fellow human, with substantially less trust.
Humans don’t trust decisions made by AIs. At least when it is pointed out that a decision is AI-based. Surprisingly, given much evidence to the contrary, humans trust humans, at least the ones in our own tribe (e.g., colleagues, fellow experts, superiors, etc..).
Dietvorst and coworkers refer to this human aversion towards non-human or algorithmic-based recommendations or forecasts as algorithmic aversion. It refers to situations where human decision makers or forecasters deliberately avoid statistical algorithm in their decision or forecasting process.
A more “modern” word for this might be AI aversion rather than algorithm aversion. However, it describes very much the same phenomena.
Okay, okay … But the above question of trust did not qualify the decision making track record of the human versus the AI. Thus respondents could have very different ideas or expectations about the success or error rates of humans and AIs respectively.
What if the fellow human expert (or superior) as well as the AI is known to have a success rate that is better than 70%. Thus more than 7 out of 10 decisions are in retrospect deemed successful (ignoring whatever that might really mean). By the same token, it also means that the error rate is 30% or less … or that 3 (or less) out of 10 decisions are deemed unsuccessful.
Based on a total of 426 surveyed respondents of which 206 was offered Question A and 220 was offered Question B. For both Human Expert (or Superior) and AI, a decision making success rate of 70% (i.e., 7 out of 10) should be assumed. Despite the identical success rate, respondents remain significantly more trusting towards decisions made by a fellow human expert (or superior) than if a decision would be made by an AI.
In a like-for like-decision making success rate, human experts or superiors are hugely preferred over a decision making AI.
A bit more than 50% of the corporate decision makers would frequently or always trust a fellow human expert recommendation or decision. Less than 20% would frequently or always trust a decision made by an AI with the same success rate as the human expert.
Thus, Humans trusts Humans and not so much AIs. Even if the specified decision making success rate is identical. It should be noted that trust in a human decision or recommendation relates to fellow human experts or superiors … thus trust towards colleagues or individuals that are part of the same corporate structure.
The result of trust in the human expert or superior with a 70% success rate is quiet similar to the previous result without a specified success rate
Based on a total of 426 surveyed respondents of which 214 was offered Question A without success rate qualification and 223 was offered was offered a Question A with a 70% success rate stipulated. As observed in this chart, and confirmed by the statistical analysis, there is no significant difference in the trust in a decision made by human expert (or superior) whether a success rate of 70% have been stipulated or no qualification had been given.
This might indicate that our human default expectations towards a human expert or superior’s recommendation or decision is around the 70% success rate.
However, for the AI-based recommendation or decision, respondents do provide a statistically different trust picture depending on whether a success rate of 70% or not have been specified. The mean sentiment increases with almost 15% by specifying that the AI has a 70% success rate. This is also very visible from the respondent data shown in the below chart;
Based on a total of 426 surveyed respondents of which 212 was offered Question B without success rate qualification and 203 was offered a Question B with a 70% success rate assumed. As observed in this chart, and confirmed by the statistical analysis, there is a substantial increase in the trust of the AI-based decision where the success rate of 70% had been stipulated compared to the question where no success rate was provided.
Respondents that would never or infrequently trust a AI-based decision is almost 20% lower when the considering a 70% success rate.
This might indicate that the human default perception of the quality of AI-based decisions or recommendations are far below the 70% success rate.
So do we as humans have higher expectations towards decisions, recommendations or forecasts based on AI than the human expert equivalent?
Based on a total of 426 surveyed respondents of which 206 was offered Question A and 220 was offered Question B. No statistical difference in the expectations towards the quality of decisions where found between human expert (or superior) and that of AI-based ones.
This survey indicates that there is no apparent statistically significant difference in what quality we expect from a human expert compared to that of an AI. The average expectation towards the quality is that less than 2 out of 10 decisions could turn out wrong (or be unsuccessful). Thus, a failure rate of 20% or less. Similar to a success rate of 80% or better.
It is well known that depending on whether a question is posed or framed in a positive way or negative can greatly affect how people will decide. Even if the positive and negative formulations are mathematically identical.
An example; you are with the doctor and he recommend an operation for your very poor hearing. Your doctor has two options when he informs you of the operations odds of success (of course he might also choose not to provide that information all together if not asked;-); Frame A. there is a 90% chance of success and you will be hearing normally again on the operated ear, Frame B. there is a 10% chance of failure and you will become completely deaf on the operated ear. Note that the success rate of 90% also imply an error rate of 10%. One may argue that the two are mathematically identical. In general many more would choose to have an operation when presented with Frame A, i.e., 90% success rate, than if confronted with Frame B, i.e., the 10% failure rate. Tversky & Kahneman identified this as framing effect, where people react differently to a given choice depending on how such a choice is presented (i.e., success vs failure). As Kahneman & Tversky’s showed, loss is felt to be more significant than the equivalent gain.
When it comes to an AI-driven decision would you trust it differently depending on whether I present you the AI’s success rate or it error rate? (i.e., the obvious answer is of course yes … but to what degree?)
When soliciting support for AI-augmentation a positive frame of its performance is (unsurprisingly) much better than the mathematically equivalent negative frame, i.e., success rate versus failure or error rate.
Human cognitive processes and biases treats losses or failures very different from success or gains. Even if the two frames are identical in terms of real world impact. More on this later when we get into some cool studies on our human brain chemistry, human behavior and Tversky & Kahneman’s wonderful prospect theory (from before we realized that oxytocin and other neuropeptides would be really cool).
HUMANS TRUST HUMANS.
Trust is the assured reliance on the character, ability, or truth of someone or something. Trust is something one gives as opposed to trustworthiness which is someone or something other being worthy of an individuals or groups trust.
The degree of which people trust each other is highly culturally determined with various degrees of penalties associated with breaking trust. Trust is also neurobiological determined and of course context dependent.
As mentioned by Paul J. Zak in his Harvard Business Review article “The Neuroscience of Trust” ; “Compared to people in low-trust companies, people in high-trust companies report: 74% less stress, 107% more energy at work, 50% higher productivity, 13% fewer sick days, 76% more engagement, 29% more satisfaction with their lives, 40% less burnout” … Trust is clearly important for corporate growth and the individuals wellbeing in a corporate setting (and I suspect anywhere really). Much of this described mathematically (and I would argue beautifully) in Paul Zak’s seminal paper “Trust & Growth” relating differences in the degree of trust as it relates to different social, legal and economic environments.
People trust people. It is also quiet clear from numerous studies that people don’t trust that much non-people (e.g., things or non-biological agents such as mathematical algorithms or AI-based) ,.. okay okay you might say … but why?
While 42 is in general a good answer … here the answer is slightly simpler … Oxytocin (not to be confused with an oxymoron). Okay okay … what is those Oxytocin and what do they have to do with trusting or not trusting AI (that is the answer). Well … if you have read Robert Sapolsky’s brilliant account for our behavior at our best and worst (i.e., “Behave: the biology of humans at our worst and best” by Robert Sapolsky) you might know enough (and even more about those nasty glucocorticoids. And if you hadn’t had enough of those please do read “Why Zebras don’t get ulcers” also by Sapolsky, you might even be able to spell it in the end).
Oxytocin is our friend when it comes to warm and cozy feelings towards each other (apart from fairly being essential for inducing labor and lactation). Particular when “each other” is part of our Team, our Partner, our kids and even our Dog. It is a hormone of the peptide type (i.e., it is relative small and consist of amino acids) and is used by neurons to communicate with each other. They pretty much influence how signals are processed by our brain and how our body reacts to external stimuli.
The higher the level of oxytocin, the more you are primed to trust your team, your stock broker, your partner (and your dog), feeling closer to your wife and your newly born babies. The more you hug, you kiss and shake hands, have sex and walk your dog, the more of Oxytocin will be rushing through your body and the more trusting you will become towards your social circles. “Usness” is great for oxytocin release (as well as a couple of other neuropeptides with a crack for making us feel better with one and another … within the confines of “Usness” … oh yeah and we have some serious gender biases there as well). Particular when “Them” are around. Social interactions are important for the oxytocin kick.
The extra bonus effect of increased oxytocin is that it appears to dampen the brain’s “freaking out” center’s (i.e., amygdala) reactivity to possible threats (real or otherwise). At least within the context of “Usness” and non-existential threats.
Thomas Baumgartner and coworkers (similar setup to other works in this field) administrated either placebo or oxytocin intranasal spray to test subjects prior to the experimental games. Two type of games where played; (a) so-called trust game with human partner interactions (i.e., human-human game) where the test subject invest an amount of money to a 3rd party (e.g., stock broker) that will invest the money and return the reward and (b) a so-called risk game of which the outcome would be machine determined by a random generator (i.e., human-machine game). The games are played over 12 rounds with result feedback to the test subject, allowing for a change in trust in the subsequent round (i.e., the player can reduce the invested money (less trust), increase (higher trust) or keep it constant (keep trust level)). Baumgartner et al found that test subjects playing the trust game (human-human game), and who received the oxytocin “sniff”, remained trusting in throughout rounds of the game, even when they had no rational (economical) reason to remain trusting. The oxytocin subjects trust behavior was found to be substantially higher compared to test subjects playing the same game having received the placebo. In the risk game (human-machine) no substantial difference were observed between oxytocin and placebo subjects which in both cases kept their trust level almost constant. While the experiments conducted are fascinating and possible elucidating towards the effects of oxytocin and social interactions, I cannot help being somewhat uncertain whether the framing of Trust vs Risk and the subtle game structure differences (i.e., trusting human experts that supposedly know what he is doing vs lottery a game of chance) could skew the results. Thus, rather than telling us whether humans trust humans more than machines or algorithms (particular the random generator kind of which trust is somewhat of an oxymoron), it tells us more how elevated levels of oxytocin make a human less sensitive to mistrust or angst for a fellow human being (that might take advantage of that trust).
It would have been a much more interesting game (imo) of both had been called a Trust Game (or Risk Game for that matter as this is obviously what it is). One game with a third party investing the test subjects transfer. Thus similar to Baumgartner’s Trust Game setup. And another game where the third party is an algorithmic “stock broker” with at least the same success rate as the first games 3rd party human. This would have avoided the framing bias (trust vs risk) and the structural differences in the game.
Unfortunately, we are not that much closer to a great explanation for why humans appear to trust humans more than algorithms. Still pretty much guessing.
And no I did not hand out cute oxytocin (and of course placebo) nasal spays to the surveyed respondents. Neither did I check for whether respondents had been doing a lot of hugging or other close-quarter social network activities which would have boosted the oxytocin levels. This will be for a follow up study.
A guess towards a possible explanation for humans being statistically significantly less trusting towards algorithms (algorithmic aversion), AI (AI aversion) and autonomous electronic-mechanistic interfaces in general, might be that our brains have not been primed to regard such as part of “Usness”. In other words there is a very big difference between trusting colleagues or peers (even if some are superiors) whom are part of your corporate “tribe” (e.g., team, unit, group, etc…) compared to an alien entity such as an AI or an algorithm could easily be construed.
So the reasons why humans trust humans and less so algorithms and AI is still somewhat reclusive although the signals are possibly there.
Based on many everyday machine learning or algorithmic applications leapfrogging our level of convenience already today … Maybe part of the “secret” is to make AI-based services and augmentation part of the everyday.
The human lack of trust in AI, or the prevalence of algorithms aversion in general as described in several articles by Berkeley Dietvorst, in a corporate sense and setting is nevertheless a very big challenge for any ideas of a mathematical corporation where mathematical algorithms are permeating all data-driven decision processes.
Berkeley J. Dietvorst, Joseph P. Simonojs and Cade Massey, “Algorithm Aversion: people erroneously avoid algorithms after seeing them err.”, Journal of Experimental Psychology: General (2014). Study on the wide spread Algorithm Aversion, i.e., human expectations towards machines are substantially higher than to fellow humans. This results in a irrational aversion of machine based recommendations versus human-based recommendation. Even though algorithmic based forecasts are on average better to much better than human based equivalent in apples by apples comparisons.
I greatly acknowledge my wife Eva Varadi for her support, patience and understanding during the creative process of creating this Blog. Without her support, I really would not be able to do this or it would take long past my expiration date to finish.
Unless otherwise specified the results presented here comes from a recent surveymonkey.com survey that was conducted between November 11th, 2017 and November 21st 2017. The Survey took on average 2 minutes and 35 seconds to complete.
The data contains 2 main survey collector groups;
Survey Monkey paid collector group run between November 11th and 14th 2017 with 352 completed responses from USA. Approximately 45% Female and 55 Male in the surveyed sample with an age distribution between 18 and 75 years of age. The average age is 48.8. The specified minimum income level was set to $75 thousand or about 27% higher than the median US real household income level in 2016. The average household income level in this survey is approx. 125 thousand annually. Ca. 90% or 316 out of the 352 respondents have heard of Artificial Intelligence (AI) previously. For AI relevant questions only the 316 was used. Surveyed respondent that had not previously heard of AI (36 out of 252) was not considered. More than 70% of the respondents had a 4-year college or graduate-level degree. About 70% of the respondents where married and 28% had children under the age of 18. Moreover, ca. 14% currently had no employment.
Social Media (e.g., Facebook, LinkedIn, Twitter, …) collector group run between November 11th and 21st 2017 and completed in total 115 responses primarily from the telecom & media industry mainly from Europe. Gender distribution comprised around 38% Female and 62% Male. The average age for this sample is 41.2. No income data is available for this group. About 96% (110) have heard of Artificial Intelligence. For AI related questions, only respondent that have confirmed they have heard about AI have been considered. Ca. 77% of the respondents have a 4-year college or graduate-level degree. 55% of the surveyed sample are married and a bit more than 50% of this surveyed group have children under 18. Less than 2% of the respondents were currently not employed.
It should be emphasized that the SurveyMonkey was a paid survey with 2.35 Euro per response, totaling 1,045 Euro for 350 responses. Each respondent completed 18 questions. Age balancing chosen to be basic and the gender balancing census.
Approximately658 corporate decision makers have been surveyed for their confidence in their own decision-making skills and their acceptance of Artificial Intelligence (A.I.) in general as well as in augmenting (or replacing) their decision making. Furthermore, the survey reveals the general perception of the corporate data-driven environment available to decision maker, e.g., the structure and perceived quality of available data.
A comprehensive overview and analysis of our AI sentiments, as it relates to corporate decision making, is provided as a function Gender, Age, Job-level, Work area and Education.
Some of the findings out of the survey;
We believe that we are all better corporate decision makers than our peers.
There is substantial gender difference in self-confidence ( or more likely over-confidence) as it relates to corporate decision making.
The higher a given individuals corporate position is, the higher is the confidence and perceived quality of that individual’s “gut feelings” compared to peers.
On average corporate decision makers are comfortable with A.I..
The higher the educational level of people the more positive is A.I. viewed.
Women seam (on average) to be slightly more reserved towards A.I. than men.
Significantly more women than men have stronger reservations against A.I..
More than 70% of corporate decision makers believe that A.I. will be important in 50% or more of a companies decisions.
In general corporate decision makers don’t trust decisions based on A.I. consultation.
Owners, Executives and C-level decision makers are substantially less trusting towards decisions made in consultation with an A.I..
More decision makers would follow an A.I. advice than trust decisions based on A.I. consultation.
More than 80% of decision makers would abandon an A.I. based recommendation if disputed by a fellow human.
Corporate Decision makers in general do not fear loosing their decision-making influence to A.I.s.
You don’t need to make an effort to find articles, blogs, social media postings, books and insights in general on how Artificial Intelligence (hereafter abbreviated A.I.) will provide wonders for all human beings, society and leapfrog corporate efficiencies and shareholder values for the ones adapting to A.I. (of which you would be pretty silly not too of course).
Somehow I cannot help wonder whether there might be a tiny paradox here? or at the very least a bit of societal challenges. By the way, challenges that in my opinion are largely ignored by policy makers and public institutions.
The challenge! How is it possible to both improve vastly the life of people and at the same time aggressively leapfrog corporate value in terms of productivity via very aggressive intelligence automation.
The question, with many different answers, is how much of work and workforce will A.I. fundamentally change and/or ultimately replace. What type of work will be impacted by A.I. and how will it shape the development of existing corporations and organizations. This issue is addressed in the 2013 paper “The future of employment: how susceptible are jobs to computerization” by Frey and Osborne. They estimated that 47% of the total number jobs in the USA (i.e., ca. 75 Million out of 160 Million) are exposed to high risk of being automated by intelligent algorithms and A.I. over the next decade or two. I suspect that given this analysis was done 4 – 5 years ago that these numbers have only grown. Frey and Osborne also clearly points out that many decision-making processes are prone to be significantly augmented by A.I. or outright being taken over (e.g., legal, health/diagnostics, fraud detection, etc..). Contrary to the past industrial revolutions that replaced menial and physical labor with machine labor. While in some parts of the world today (e.g., China) human factory workers are being massively replaced by robots and intelligent automation in general (e.g., reporting a quantum leap in productivity), this time around also highly specialized and cognitive intensive jobs, requiring college or graduate-level degrees, are at risk to be replaced by A.I..
It is wise to keep the Friedman doctrine in mind stating that “The social responsibility of Business is to increase its profits“ (i.e., Milton Friedman, New York Times Magazine, 1973 … Milton was not a great believer in corporate social responsibility in a milenia sense I guess;-). In other words, a corporation’s only goal is to increase its profits within the rules of law. Following this doctrine, it might be compelling to pursue aggressive A.I.-driven automation leading to workforce reduction and ultimate replacement (e.g., China manufacturing).
Obviously, today through taxes (in general) and salaries, it is possible to maintain a degree of social responsibility. Albeit indirectly by individuals working for corporations or businesses. In case of the zero-human-touch corporations resulting from a structural replacement of human labor by A.I.s, the indirect path to social responsibility might disappear. Assuming such a corporate strategy really would optimize profit sustainable over time rather than cost internal structures. I suspect that one of the bigger challenges to society will be that it is very possible on a local level to hugely maximize profit by zero-human-touch corporations, e.g., China manufacturing aggressively pursuing automation. Profit maximization can be maintained, as long as goods or services are sold somewhere else with a stable socio-economical fabric (i.e., geographical arbitrage) or there exist a group for people on a local level not impacted by loss of work and income. Obviously, if your workers are an intregral part of your business model, massively laying them off might not be the best idea for profit maximization (i.e., who cares that you have slashed 50% of your cost if nobody can afford to buy your product or service because you put them out of work).
The intelligent machine age will see the remaining part of factory workers being replaced by A.I.. Also many tasks requiring a high degree of cognitive involvement, as well higher education, will be augmented and eventually replaced by A.I..
Having a graduate-degree might soon no longer be a guaranty for job security.
CORPORATE DECISION MAKING – HUMAN VS A.I. OR HUMAN + A.I.?
From a corporate decision making perspective there are two main directions to take (and a mixture in between);
A.I. augment the corporate decision makers in their decisions.
A.I. takes over major parts of the corporate decision-making process.
And so … it begins …
I got really intrigued by a recent article in Harvard Business Review titled “AI May Soon Replace Even the Most Elite Consultants” by Barry Libert and Megan Beck (both consultants/advisors) making the case that A.I. could replace role of elite consultants as they are supposedly used today. One of my favorite quotes from this thought provoking article is “Perhaps sooner than we think, CEOs could be asking, “Alexa, what is my product line profitability?” or “Which customers should I target, and how?” rather than calling on elite consultants”. I really hope that a CEO would not need an EliteConsultants for such answers … but it might be true, that corporations frequently use expensive consultants for what turns out to be through roughly silly tasks.
Obviously … the cynic in me says … The CEO could not only save the expensive elite consultant but also considerable internal elite resources; e.g., CMO, Sales Director, Marketing Managers, Pricing Specialists, Financial Controllers, etc… (just to name a few in the corporate food chain). That sounds pretty cool! Imagine the salaries and cost that could be saved here! Wow! … Though, I suspect that he (i.e., the CEO) might still need some (new) elite & likely hilarious expensive A.I. consultants instead (maybe Barry and Megan would be up for that task;-)?
Moreover, there is an inherent assumption in the assertion that most corporate decisions, or at least the important ones taking up the time of CxOs and senior management, are coming with high quality voluminous amount of data that would naturally lend it to a data-driven algorithmic augment decision process. In my opinion this is far from the reality. Many decisions that corporate decision-makers are bound to make will be based on tiny to small amounts of often uncertain or highly outdated data. Thus lending itself poorly to the typical arsenal of data-driven decision making and big-data based algorithmic approaches. The assumptions made, backing up corporate decisions, will be based on “gut feelings” (backed up by excel and nice powerpoints), theory of (corporate) mind (no) and largely directional rather than hard science. There is obviously nothing that hinders decision makers from applying the same approaches we would apply to ideal data-driven analysis and decision making, as long as the decision-maker understand the limitations, risk and uncertainty that such an approach would bring in the context of a given decision. Particular when the underlying data is tiny to small and inherently of poor quality (e.g., because of age, uncertainty, apples and bananas, out-of-context, …).
Bridgewater Associates LP, the largest hedge-fund company in the world with ca. 1,700 employees and 150 Billion US$ of assets under management, is currently working on automating most of the firm’s management. It would appear that one of the most important roles of the current workforce in Bridgewater is to provide the training ground for a corporate A.I. that can take over the management of that workforce. This vision and strategy is the brain child of Ray Dalio, the founder, chairman and co-CIO, of Bridgewater Associates.
What about replacing the CEO with an AI? … run everything as a DAO or distributed autonomous organization based on smart contracts and orchestrated by a Chief Artificial Officer (CAO). Sounds even more like science fiction (or a horror movie pending on tastes) … but Jack Ma, founder of Alibaba, have speculated that within the next 30 years, the Time Magazine cover for the best CEO of the year will likely be an A.I. or CAO.
So maybe the future looks more like this;
Will Intelligent Algorithms make CEOs irrelevant in the not too distant future?
Will the CEO be replaced by the CAO? … WTF! … Well, time will show!
With the adaptation of intelligent algorithms and corporate-wide pursuit of aggressive automation, how will an A.I.-augmented organization look like? Josh Sullivan and Angela Zutavern in their wonderful book, particular for a person having a degree in mathematics and physics, “The mathematical corporation: where machine intelligence and human ingenuity achieve the impossible” provides a vision of how this next generation corporation might look like … The Mathematical Corporation … it is a place where algorithmic augmented decision making is intimately integrated into the corporate decision-making process … I am not 100% (maybe not even 20%) convinced that the term “Mathematical Corporation” will find wide jubilance (with the possible exception of STEM folks … maybe) … If I am wrong, I would argue that this already is on the way to achieving the impossible.
The world of the Mathematical Corporation is a world where the human decision making is augmented, as opposed to replaced, by mathematics or algorithm applied to huge amount of available data (that no mortal human could possible make sense off in the same comprehensive way as a mathematical algorithm) … It is a positive world for Homo sapiens sapiens, or at least for the ones who are able to adapt and become Homo Sapiens Digitalis Intelligere … Sullivan and Zutavern states: “The supercharged human ingenuity youwill wield in the real world will stem from the thought-like operation of machines in the digital one.” (emphasis my own) … and then the caveat … “Only leaders who learn to assemble the pieces and tap their potential will realize the benefits of this marriage, however” (emphasis my own). … Sooo … the future is bright for the ones who are able (and willing) to become the New Human augmented by Digital Intelligence … For the rest … please read Charles Darwin and pray for universal basic income.
Again (and again) we meet the inherent assumption that most corporate decisions can be fitted into a ideal data-driven algorithmic process lending themselves “easily” to A.I.. This does not fit the reality of many corporate decisions including many important and critical ones. For the applied machine learning practitioners, with “dirt” on their hands (and up their elbows), in practical terms know that there is nothing easy about getting data prepared for machine learning … its hard work with no instant success formula. It is a largely iterative and manual (labor intensive) process to come to a result that is actually applicable to real world problems.
But wait a minute … how likely is it that decision makers will actually adapt towards a mathematical corporation? Will they actually trust and follow A.I.-based recommendations or just discard such “foolishness”?
Algorithmic aversion may turn ugly and become an A.I. allergy among workforces that stands to be replaced or “upgraded” by augmentation.
The question really is whether applying an algorithmic approach to tiny or small data amounts still provide a better basis for decisions than leaving it out completely. In other words augmenting the decision makers own wetware cognitive decision process and inferences often based on theory of (corporate) minds.
Let us first establish that even relative simple mathematical forecasting procedures and algorithms are providing for better decisions and insights than if based purely on human intuition and experience. In other words; algorithmic approached, even simple ones, will augment a human-based decision (although I will also immediately say that it assumes that the algoritmic approach has been correctly implemented, its inherent uncertainty, error rate and bias have all been correctly considered … sorry even here there is no “free lunch”).
Algorithms, even simple ones, does perform better than human beings limited to their own cognitive abilities in terms of predictions (i.e., an essential part of decision making whether done consciously or subconsciously). This result has been confirmed many times over by the likes of Paul Meehl, Robyn Dawes and many other researchers in the last 50 – 60 years. Importantly though, machine learning algorithms does not offer an error free approach to decisions making. However, algorithmic approaches does offer predictions and solutions with lower, often superior, error rates (and not unimportantly … quantifiable error rates) than what would be the case of pure cognition based decisions.
No wonder Homo sapiens sapiens have grounds to be allergic towards intelligent algorithms … Most of us have problems with peers being smarter than us … although this luckily happens extremely rarely as we will see in the data out of the Survey presented below (or at least if you happen to ask peoples own opinion). The challenge around algorithm aversion is address by Berkeley Dietvorst el al. in a more recent 2014 paper “Algorithm Aversion: people erroneously avoid algorithms after seeing them err” (see references in the paper as well). This paper in detail addresses algorithmic aversion in experts and laypeople. People in general remain very resistant in adapting more mathematical approaches despite such being demonstrably less prone to error than human-based decision making without algorithmic augmentation. This holds true for simple algorithmic approaches as well as for example explored to great length by Robyn Dawes and co-workers. As argued in the paper of Dietvorst et al “we know very little about when and why people exhibit algorithmic aversion” … However, one thing is very clear;
We, as humans, are much less forgiving when it comes to machine errors than human errors.
The standard we expect of artificial intelligence are substantially higher than what we would require from a fellow human being or co-worker.
However, it is also true that minds and culture changes often in synchronicity and that what was unthinkable a time ago can be the new normality some a time after.
And obviously even the best algorithmic approaches or the smartest A.I. implementations will make errors. Either because we are at the limit of Bayes optimal error or due to the limitations of the training that was applied in the algorithmic learning process … Bad Robot! … That obviously is not the point. Humans make mistakes and errs as well. We are prone to “tons” of various cognitive biases (as has been described so well by Kahneman & Tversky back in the 80s) and are pretty lousy at handling too much complexity.
What? … Bad at handling complexity? Well … Yes, we are! In general, the human mind appear to have a capacity limit for processing information around the 7 information chunks or pieces. Plus or Minus 2. As George Miller describes in his 1956 influential paper “The Magical Number Seven, Plus or Minus Two Some Limits on Our Capacity for Processing Information”. Since Miller’s work back in 1956, the magic number is still around 7 (or 4 – 11), albeit we are having a more nuanced view on how to group informational chunks together to effectively increase our handling of complex problems. Isn’t this just of academic interest … Liraz Margalit, Head of Behavioral Research at Clicktale, back in July wrote a wonderful blog, backed up by experimental evidence, on how choices can become overwhelming and why it make sense for business to make it easier for customers to choose (see “Choices can become overwhelming, so make it easier for customers”). I wish Telco’s and other online retailers would follow Liraz’s advice of simplifying the options presented to the potential online customer. The complexity of presenting or recommendation can all be dealt with easily in the background by an intelligent algorithm (i.e., A.I.).
Does intelligent algorithms, or A.I., suffer from similar limitations in complexity handling or from a gazillion cognitive biases? Handling complexity … obviously not … I hope we do agree here … So what about biases introducing errors in the decision process (note: bias here not in a machine learning sense, which implies under-fitting to available data, but in the more expansive sense of the word)? Sure algorithms can be (and possibly often are to an extend) “biased” in the sense of a systematic error introduced in training the algorithm, by for example unfair sampling of a population (e.g., leaving out results of women or singling out groups of a population ignoring data from the remainder, etc..). Often algorithmic biases can be introduced un-intentionally simply by the structure of the data used for training the A.I.. Some recent accounts for A.I. biases are the provided by Motherboard which found that Google’s sentiment analyzer thinks being Gay is bad or that training data had been labeled (by humans) in way that would teach the A.I. to be sexist and racist. An Example of potential A.I. bias: For corporate decision making it would not be too strange that past training data would reflect a dominance of male decisions. It is a scientific well established fact that men more frequently make decisions even if a decision would be counterproductive or irrational (in terms of risk and value). Men are prone to a higher degree of over-confidence in their decision making that results in higher losses (or less gains) over time compared to women. Thus, using training data dominantly representing male corporate decisions might to a degree naturally bias the A.I. algorithm towards a similar male dominated decision logic. Unless great care is taken in de-biasing data, which might mean much less available for training, or using synthesized data of idealized rational decision logic (i.e., much easier said than done). Furthermore, given humans are very good at post-rationalizing bad decisions, the danger might anyway be that available data labelled by human decision makers might not be entirely free of bias itself irrespectively.
Human biases are often acquired within a cultural context and by the underlying neurological workings of our brains. So overall there are good reasons why mathematical algorithms outperform, or at the very least match, in most situations the human decision maker or forecaster. For a wonderful account of the neurobiology of human behavior do find time to read Robert Sapolsky’s “Behave: the biology of Humans at our best and worst” who provide a comprehensive (and entertaining) account to some of the fundamental reasons why we humans behave as we do and probably can do very little about it (btw. I recommend the audible version as well which is brilliantly read by Michael Goldstrom).
Another reason for a potential A.I. aversion among decision makers (if being faster, better and more accurate should not be enough reason) is the argument that we don’t understand what is going on inside the applied machine learning algorithms. For a majority of decision-makers, not having had exposure to reasonably advanced mathematics or simply don’t care much about that discipline, even simpler algorithms might be difficult to understand. And it quickly get much more complex from there (e.g., deep learning field). It might not really matter much that some of the worlds top A.I. experts argues that understanding does not matter and it is okay to use intelligent algorithms in a black box sense.
The cynic in me (mea culpa) would argue that most decision makers don’t understand their own brain very well (or might not even be consciously aware of its role in decision making;-) and that certainly doesn’t prevent them from making many decisions. In this sense the brain is a black box. So A.I. performance and its capability of handling large and complex data volumes should be a pretty good reason for not worrying too much about understanding the process of A.I. reasoning.
Why? Because my A.I. says so! (not entirely comfortable with that either I guess).
In summary why are humans might be prone to A.I. allergy or algorithmic aversion, apart from we don’t like ‘smart-asses’;
A.I. is much better at handling large-scale complexity than humans (i.e., human limit seems to be somewhere between 4 – 11 chunks of information).
A.I. is likely to be substantially less biased compared to the pletfore of human cognitive and societal biases.
A.I. would take the fun part out of decision making (e.g., risk taking and the anticipatory reward).
A.I. is a threat to our jobs (whether a perceived or real threat does not really matter).
Humans do not like (get’s very uncomfortable with) what they do not understand (at least if they are concious about it, e.g., our brains is usually not a big issue for us).
It is clear that with the trend of increasingly computer and storage power at increasingly lower cost, married with highly affordable ubiquitous broadband coverage (i.e., fixed and mobile), twice married with an insane amount of data readily available in the digital domain, algorithmic approaches providing increased convenience and augmentation of every day civil as well as corporate life becomes highly attractive.
The development of A.I. performance is likely going to increase in a super-linear fashion following improvements in computer and storage performance. The wet biological brain of homo sapiens sapiens not so much (obviously).
It is no longer unthinkable, nor too far-out in the future, that blockchain-enabled decentralized autonomous organization technologies (i.e., DAOs) combined with a very high degree of A.I.-driven automation could result in almost zero-human-touch corporations. Matthew Mather has described a possible darker future based on such principles in his super exciting novel “Darknet”, where a A.I.-boosted DAO conspire become a world dominating business with presidential aspirations (there might be some upside to that scenario compared to todays political reality around the world … hmmm).
So where does all this leave us … Homo Sapiens Sapiens?
How will algorithms and complex mathematics change corporate decision makings that today is exclusively done with the help of a beautiful complex biological machine … the human brain.
Might there be a corporate advantage of augmenting or maybe eventually replacing the emotional neurobiological homo sapiens sapiens brain, with an A.I.-driven digital brain?
Assuming we will have a choice … will we, as humans, accept being augmented by Digital Rainmen’s? … Will the CxOs and upper management stop thinking and exclusively make use of the Digital Intelligence, the A.I., available to them in the near- to medium-term future? (note: near and medium could still be far away in some A.I. gurus opinions).
Lot of questions! Time to try to get some answers!
To gauge corporate manager’s perception of their own wet brain decision making capability, their decision making corporate environment and their opinion of having their decision making process augmented by A.I., I designed a 3 – 4 minute survey with SurveyMonkey.com.
The survey consist of 24 questions and takes on average a little less than 4 minutes to complete. The questions are structured around 4 main themes;
General information about you (e.g., gender, age, job level, education level).
Your corporate decision making skills.
The quality of data used in your decision-making process.
Your acceptance of A.I. as it related to your corporate decision-making processes.
Over the cause of the data presented here, I have collected 658 responses over 3 groups of Collectors streams open collection data from various sources.
Collector Group 1 (CG1): SurveyMonkey Audience Response option. SurveyMonkey in this case gathered responses from 354 respondents in United States between 18 – 100+ years of age and with an income above 75 thousand US$. Age balancing was basic and the Gender balancing based on Census. The data was collected between September 3rd and September 6th 2017. This is a paid service with the cost of approximately 1,040 Euro or 3 Euro per respondent. From a statistics perspective this is the most diverse or least biased (apart from being from the USA) responses data used in this work. When I talk about the Reference Group (RG), this would be the group I refer to.
Collector Group 2 (CG2): My own social media connections from LinkedIn, Facebook and Twitter. This is approximately 113 responses. This sample is largely gender skewed towards male (62 males vs 31 females). Furthermore, a majority of responses here have a background in telecommunications and media industry. Most of this sample consist of respondents with a graduate-level degree (77) or a 4-year college degree (19).
Collector Group 3 (CG3): This group consist of 191 responses primarily from the European telecom industry (but does not overlap with CG2). Again this response sample is largely biased towards males (156 responses) with a 4-year college degree or graduate-level degree (128 responses).
The data will be made available on GitHub allowing others to reproduce the conclusions made here as well as provide other insights than addressed in this blog.
For this Blog, I will focus on the survey results across the above 3 Collector groups and will not discuss the individual groups with the only exception of SurveyMonkey’s own Collector, i.e., the Reference Group. Irrespective, the 3 Group responses are statistically largely similar (i.e., within the 95-percentile) in their response distributions with a very few exceptions.
Out of the Reference Group, 50 respondents identified themselves as Retired. These responses have not been considered in the analysis. In the SurveyMonkey audience response (i.e., the Reference Group), 95 respondent did not match the provided current job level options and choose the Other category with an alternative specification.
Thus 608 responses to the Survey are left for further analysis.
After filtering out retirees, we are left with 608 respondents in the Survey. The Reference Group have a reasonably balanced gender mix of 46% female and 54% male. The other Collector groups CG2 and CG3 are much more skewed towards male (e.g., 27% and 18% female mix respectively). The reason for this bias is the substantial lower representation of women in technology heavy units of the telecom & media industry which is represented in CG2 and CG3.
However, both in its totality as well as in the separate Collector Groups are there sufficient Gender-based data to make statistically valid inferences within a level of 95% confidence.
JOB LEVEL DISTRIBUTION
The purpose of this Survey was to try to capture corporate management decision making. This is very nicely reflected in the job-level distribution of the participating respondents.
At least 335 (or 55%) of the respondents are Middle Management or higher. In total 80 (or 13%) characterizes their current job level as Owner, Executive or C-Level management.
The absolute numbers per job-level category above does allow us to statistically analyze the possible differences in corporate decision making perception, sensitivity towards A.I. in general and A.I.-driven augmentation in particular between the different management categories sampled here.
In this question of job level, women are under-represented (compared to their overall share of respondents, i.e., 34%) in the senior and middle management categories with 25% and 30%. This bias is also present in the Reference Group with women also being under-represented in the “Owner/Executive/C-level” category.
Does a C-level leader perceive A.I.-augmented decision making differently than a senior manager? and what about those two categories compared to middle management?
The educational level of the respondent to this survey is very high. More than 70% of the respondents have a 4-year college degree of higher. 47% have a graduate-level degree. This might be important to consider when we get deeper into opinions of decision making and A.I. sentiment.
The absolute response numbers for “Primary school” (3) and “Some high school, but no diploma” (17) are not sufficiently high to carry statistical significance in comparative analysis. Those distributions are on an individual level not considered for any conclusions or comparative inferences.
The average age of this surveys respondents is approximately 45 years of age. The age distribution between males and females are very similar. It is clear that the sample has a definite age bias. This is reflected across all the Collector Groups including the Reference Group, where the average age is closer to 47 after the Retired Group has been filtered out.
Note that the absolute response numbers for age groups “17 or younger” (2) and “18 – 20” (4) are not sufficiently high to carry statistical significance in comparative analysis.
The cynic might question why it is so relevant to understand the opinion and sentiment towards A.I. in a sample which such a relative high age.
Over the next 10 years it is likely that many of those in the group below 55 will either remain their management functions or have been promoted to senior management or executive/C-level. Even the Mark Zuckerbergs of today does age (i.e., Mark Z will in 10 years time be 43 and Yann LeCun 67 and I just had age selective amnesia …). Thus their decision making skills would still be largely in use over a period where A.I. is likely to become an increasingly important tool in the corporate decision making process. Augmenting and in many areas replacing the human decision maker.
WE ARE ALL BETTER CORPORATE DECISION MAKERS THAN OUR PEERS.
It is a well established “fact” that we humans are all less risky and more skillful drivers than our fellow drivers. This was systematically confirmed in the seminal paper by Ola Svenson back in 1981. Well at least we as individuals pretty much all believe so (allegedly) … I certainly do too, so others must be wrong! ;-). In the study by Svenson, 88% of US participants in the research believed themselves to be safer than the median (i.e., frequency distribution midpoint or 50% of quantities falls below and 50% above). Talk about self-confidence or maybe more accurately over-confidence.
So to paraphrase Ola Svenson statement into a question relevant for corporate decision making … Are we as corporate decision makers better at making less risky and much better decisions than our peers?
And the answer is an overwhelming … YES! (even if it of course makes little statistical or reality sense).
We are as corporate decision makers all (or almost all) better than our peers. At least that is our perception.
Only! 3% (THREE PERCENT) ranked their decision making skills below average. 54% above average. If you impose a normal distribution it would even be reasonably fair to state that ca. 75% of respondents assess their corporate decision making skills to be better than their peers (or above the median in a statistical sense).
It is interesting, although not surprising or novel, that male self-confidence in general is higher than that of female respondents. Of course self-confidence is a very nice (too nice maybe) word for over-confidence in one self’s own abilities to make good or better decisions.
Statistically, only for CG1 (i.e., the MonkeySurvey audience response) is the overall response distribution for female statistical significantly lower (at 95% confidence) compared to that of male respondents. In other words, females are to a lesser degree than males over-confident in terms of their own decision making skills.
There are several perspectives on gender differences as it relates to confidence (call it self-confidence or over-confidence). We have the classical work by Maccoby and Jacklin (“The Psychology of Sex Differences” from 1974) which take is in my opinion somewhat a pessimistic outlook or outdated since the time of their exhaustive research work: “Lack of self-confidence in achievement-related tasks is, indeed, a rather general feminine trait. The problem may lie, at least in part, in the tendency for women to perceive themselves as having less control over their own fates than do men”. Sarah Burnett (contemporary to Maccoby & Jacklin) in her beautiful account (yeah I like it better;-) for gender differences and self-confidence (from 1978) “Where Angels fear to tread: An analysis of sex differences in self-confidence” concludes; “If there is a “real” sex difference in self-confidence, it could well lie in the fact that women are reluctant to forecast success for themselves in the absence of reliable supporting evidence; men, perhaps because of their wider range of experiences, their “machismo,” their penchant for risk, or whatever, seem less hesitant.”.
Finally, I want to refer to an equally interesting account for gender differences as it relates self-confidence and implied risks. Barber and Odean’s super interesting article “Boys will be boys: Gender, Overconfidence, and common stock investments” that based on stock trading behavior clearly shows that men are significantly more confident than women in their ability to choose the “winning” stock. The work of Barber and Odean (as well as other works in this field) also shows that men in general incur higher loses than women investors. This have been attributed to the substantially higher degree of (and statistically significantly) male over-confidence compared to that of females.
And oh boy! … if you are very brave, let an adolescent male, or a single male, make your corporate decisions. Your might be in for a really interestingly scary ride. Why? your typical adolescent person, between ca. 15 – 25ish year of age, has an under-developed frontal cortex & executive system. Simply put an adolescent does not have enough rational control to put up red flags when engaging in risk taking. Single or unmated people have in general lower levels of oxytocin and vasopressin (i.e., neuropeptides) compared to what is found in couples. Both vasopressin and oxytocin are known to lower or moderate risk-taking and increased pro-social behavior (e.g., particular true for males).
Both men and women are subject to substantial over-confidence in their corporate decision making skills.
Men shows a higher degree of over-confidence, compared to women, in their corporate decision making skills.
Women working in a male dominated environment (e.g., engineering) are at least as over-confident in their abilities to make corporate decisions as their male peers.
Talk about a gender gap!?
So which job-level group have the highest opinion about their own corporate decision making skills? Which group have the overwhelmingly largest degree of over-confidence (or self-confidence if we want to be nice) bias across all job levels? … hmmmm …
Well, no surprise (maybe?) .. Owners, Executives and C-level leaders outshine all other job levels by their decision-making confidence compared to their peers. Interestingly not only is the average significantly higher for Owners/Executives/C-level respondents but also their variation (“collective doubt”) was significantly lower than any other job-level group.
It is not only that “Boys will be Boys” should worried us … maybe “CEOs will be CEOs” should as well? 😉
Also a reasonable clear trend (with exception of Senior Management and Middle Management which are statistically similar). The lower an individual is, in the corporate hierarchy, the less expressed self-confidence does that individual appear to have, e.g., Entry-level managers at 60% and Executive at 89%, a staggering difference of almost 30% in self-confidence.
The higher in the corporate hierarchy an individual is, the higher is that individuals degree of confidence in her or his decision-making skills.
So we have established that just like individuals confidence in their own driving skills compared to peers, the same appears to hold true for corporate decision making skills. We are all better than our peers. But what about decisions based on that wonderful “gut feeling” or intuition … or as I have often heard it expressed: “I feel it on my water” (no further elaboration will be given).
According with Wiktionary: “gut feeling (plural gut feelings) (idiomatic) an instinct or intuition; an immediate or basic feeling or reaction without a logical rationale.” .
The characteristics of gut feelings, instinct or intuition are;
Arrive rapidly without deliberate rational thought.
Triggered by past experience and learnings.
A sense of confidence and trust in feelings.
Difficult to rationalize.
“Behind the scene” or a sense of sub-conscious process.
Ultimately, what is going on with our ‘gut feelings’ is thought to be a result of an intimate play between the autonomous nervous system, the (ventromedial) prefrontal cortex and amygdala (among other actors in the limbic system). It is believed to be manifestation of bodily feelings associated with emotions. This could be a heartbeat pulse increase, an uneasily feeling in the gut, ‘goosebumps’, etc… It is that feeling in the body we get in case of unease, discovery, confronted with unexpectedness, and so forth. Thus there is a well established (although maybe less well understood) Brain – Body coupling or feedback that is responsible for those bodily feelings that signals to the brain to be on lookout for the immediate future. This process has been well describe in Antonio Damasio’s 1994 book “Descartes’ Error: Emotion, Reason and the Human Brain” (and in countless scientific publications since then). Another way of looking at gut feelings or gut instinct is Daniel Kahneman’s dual-system of fast and slow thinking. With the fast system in many ways being a metaphor for that gut feeling or intuition. This is often also called the affect heuristic which allows us to very rapidly make decisions or solve problems.
Depending on what emotional state the gut feelings are associated with, can greatly influence the decision making of individuals. There are many situations where gut instinct or feelings are beneficial for the decision maker. As has been argued by Antonio Damasio and others; “emotionless decisions are not default good decisions” and of course there is the too much of a good thing “too many feelings/emotins are also detrimental to good decisions” (e.g., people are terrible decision makers under stress). There need to be a proper balance between the minds affective processes (i.e., typically residing within the limbic system) and that of the frontal cortex cognitive-controlled processes.
Much of the data-driven philosophy including the ideas around the mathematical corporation, is to decouple emotions and feelings from the decision making process. After all an algorithm doesnt have that intricate play between emotion, feeling and “rational” reasoning a human does (e.g., it doesnt have a limbic system). An A.I. may not be burdened by a sh*t load of cognitive biases in its decision making process (note: it does not mean an A.I. cannot be biased, it most often will be if the data it has been subjected to are biased … which most data typically will be). So that is swell! …?… Maybe not! As Antonio Damasio has shown lack of emotions can easily paralize or mess up decision-making (see his “Descarted’ Error: …” or study psychopaths decision-making).
So … How prevalent is decision-making based on instinct or gut feelings? (or how willing are respondents to admit that they are using feelings, instinct or sense of direction in this super duper data-driven world of ours … or at least the aspiration of a data-driven decision making world).
The above response shows that in a bit more than 50% of business decisions taken relies (to an extend) on gut feelings. I should point out that within the surveyed response data there is no clear statistical evidence of difference between different sub-groups (e.g., male vs female, job-level, education).
I refrain from passing judgement on the above surveyed result, as I can say that I have, as a scientist, benefitted from such gut feelings or intuitive leaps in the past. I do think it is important to point out as this process remains an integral part of most human decisions, irrespective of our business have become increasingly data-driven (or mathematical).
Gary Klein (in “Sources of power: how people make decisions”) estimates that in 80 plus percent of time-pressured situations decision makers rely on intuition or gut feelings rather than deliberate rational choice. Burke & Miller in their 1999 paper “Taking the mystery out of intuitive decision making” surveyed 60 experienced professionals holding significant positions in major organizations across various industries in the US. Burke and Miller’s survey results was that 12% of surveyed professionals answered that they always used intuition in their decision making, 47% often, 30% sometimes, 7% seldom and 3% rarely. This is not that different from the reported survey results above on the frequency of the use of gut feelings in corporate decision making (although the scales might not be completely comparable).
So how do we assess the quality of our “gut feelings” in comparison with our peers?
Maybe not too surprising, as Question 10 closely resembles that of Question 7, respondents in general perceive their gut feelings as being better than their peers.
The maybe interesting observation here is that the gender difference is not statistically apparent from the responses to Question 10. While there where a clear statistical difference in the self-confidence (i.e., Question 7) between women and men, this is not apparent in the self-judgement of the qualities of gut feeling in comparison to peers.
Parroting the decision-making skill confidence question (i.e., Question 7), the survey data on the quality of ones own “gut feelings” do indicate a dependency of role in the corporate hierarchy. The higher the corporate position the higher is the “gut feelings” quality perceived in comparison with peers.
When it comes to self-assessment of an individual’s “gut feelings” quality compared to peers there are no apparent gender difference.
The higher a given individual’s corporate position is, the higher is the confidence in or perceived quality of the individuals “gut feelings” compared to peers.
Finally, do corporate decision makers like to make decisions?
Overwhelmingly, respondents do like or enjoy making (corporate) decisions. I should point out that the question posed here might be leading to answers towards the positive end of decision-making sentiment. In retrospect the Question 6 could have been asked in a more neutral fashion (e.g., “How do you feel about making decisions relevant to your company” or alike).
Why is it relevant to understand individuals self-confidence in own and sentiment towards decision making?
First of all it might reveal an uncomfortable degree of over-confidence in corporate decision making that more algorithmic approaches could address. It might point towards a substantial degree of bias in the corporate decision-making process ignoring in practice relevant available data. Again A.I. methodologies might provide for a more balanced decision making process by neutralizing some of that individualized bias that typically overweight corporate decisions. On a very basic level it might further provide some realistic expectations to general adaptations of algorithmic approaches to data driven decision making. Successful A.I. policy and strategy certainly would stand or fall with individual decision makers perception of value to them as individuals as well as the corporation they are paid to manage and lead.
CORPORATE DATA-DRIVEN DECISION MAKING.
The newish buzz of the corporation (unless you are with Amazon, Facebook or Google it is a pretty old buzz) … data-driven decision making, algorithmic augmentations to data analysis and resulting decision making, the move towards the so-called mathematical organization are resulting in expressed (or unexpressed) strategies (but often very little or poor policies) that permeates medium and large corporations today (and pretty much non-existing for small ones).
The impression we “corporate peasants” are often given by (some) A.I. Gurus (and usually affiliated with Management Consulting or from firms light-years ahead of the pack) is that in the near-future algorithmic approaches will be able to substantially augment and in many instances replace decision-making processes and makers. That all should be data-driven and that data-driven decision making is the holy grail. The A.I. Gurus are often acting as the new Latin speakers of the Age of Enlightenment (for the ones enjoying the satirical plays of the Enlightenment have a look at Ludwig Holberg’s “Erasmus Montanus” written in 1722).
The fact is that many corporate decisions, even important ones, are not or cannot be based on huge amounts relevant data. Often data is not available or simply not relevant or outdated. Applying algorithmic approaches or machine learning approaches might be highly in-efficient and lead to a false sense of comfort that more human-driven decisions may not suffer from (although there is likely a whole host of other biases playing role irrespective).
Human decision makers make mistakes (males more than females). The more decisions the more mistakes. Such mistakes can be costly. Even catastrophically so. Often the root cause is that the human decision maker is over-confident (possible to the extreme as we have seen above) in his or hers ability to make good decisions considering the associated risks and uncertainties.
Generalization from small or tiny data is something the human brain is a master of, even when the brain demonstrably / probabilistically has no basis for such generalizations.
When confronted with large and huge amounts of often complex data, cognitively the human brain simply cannot cope and will dramatically simplify down to a level that it can handle.
Anyway, lets break the data-driven decision making down into the available data, the structure of the available data and of course the perceived quality.
About 70% or more of the respondents are frequently (ca. >70% of decisions) to always using available data in their decision making process. After all why would a decision maker not use available data? … Well it might depend on the quality of that data! … and to be fair, from the question it is difficult to gauge with what weight available data is included in the decision process vis a vis gut feelings and other cognitive biases, e.g., over-confidence in interpretation of available data.
Most corporate decision makers consider available data for most of their decisions.
So that’s great when data is available. What about how frequently data is actually available to be considered in the decision-making process?
So data would be available for about 57% of the decision-makers in at least 70% of their decisions. While 31% of respondents always consider available data in almost all of their decisions (i.e., Question 11), only 13% of respondents have data available for almost all their corporate decision the need to make.
A little more than half of corporate decision makers have data available for most of their decisions.
Ca. 30% of the surveyed respondents have data available for half their decisions. However, only 19% of the respondents consider the available data for approximately half of their decisions.
There is a relative large disconnect between data being available for corporate decision making and the data actually being used.
This might indicate several issues in the data-driven decision process
Available data is perceived as poor quality.
Available data is perceived as being too complex to contribute to the decision process.
A certain degree of immaturity in how to include data in the decision process.
Too high reliance on gut feelings and overconfidence bias ignoring available data.
Let us have a look at the perceived quality of the available data;
From a the above categorization of data quality, one would expect that a little less than 40% of respondents would have a possible straight forward path to a data-driven decision of reasonable to high quality. Approximately 60% or more would either not be able to rely on an algorithmic data-driven process. Or if pursuing this venue, would need to be very careful in their interpretation of the available data, analysis based on this data. they should be expecting a relative high degree of uncertainty and systemic risk in their decision. Particular comparative scenarios or alternatives, often considered in corporate decisions, could be rather meaningless if the underlying data is of relative poor quality. A data-driven or mathematical decision process will not change that.
A majority of corporate decisions relies on data that might not be very well suited for advanced data-driven algorithmic approaches.
GIGO (i.e., garbage in garbage out) is still a very valid dogma even in a data-driven decision-making process augmented by algorithm or other mathematical tools.
When it comes to important decisions, 50+% of the respondents corporate decisions relying on are either large-data (46%) or big-data driven (6%). The glass half full perspective is that for at least half of all important corporate decisions this does bodes well. It should be possible to apply advanced algorithms or machine learning approaches that would augment the human decision-making process. The glass half empty perspective is that for the other half of important decisions we may not have such luck that the mathematical corporate philosophy could offer. The challenge obviously is how relevant mathematical approaches can be to important corporate decisions where on small-, tiny- or no relevant data is available. Would the application of pre-trained data models, trained on larger but non-related data amounts, be of use. Maybe this remains a domain where “wishful” thinking models (e.g., normal business models & business cases), gut feelings and inflated self-confidence would be the prevalent method to come to a decision.
Would it not be great if your competition no higher quality data available for their decision making processes than available to your business. At least if you have a level playing field, in terms of available data and the quality of such data is about the same, the rest would be up to the ingenuity of respective decision makers including the quality of applied algorithmic processes.
Compared to respondents self-assessment of their own decision making skills and quality of their gut feelings (compared to peers), they appear more careful in judging the quality of their corporate competitors. 74% assess that there is little difference in the underlying quality of data available to their competitors and their own decision-making process.
Most corporate decision makers expects business competitors data to be of the same quality as available to themselves.
It is easy to loose sight of the human opinion when discussing data-driven decision processes and decision-making. Particular as such processes becomes more automated and enhanced by algorithmic or applied machine learning approaches. It might become easy to ignore human insights, when you have a solid mathematical-statistical framework to process you data and provide recommendations for the best decisions based on available data. Taking the data-driven organization to the possible extreme.
How important is human insights or human opinion augmentations to data-driven insights?
The glass is half full interpretation of the above result out of this survey, would be that more than 50% of the respondents find it important to consider human opinions beyond what comes out a their data-driven process. In other words enrich their data-driven analysis with (alternative) human opinions.
The glass is half empty interpretation is that almost 50% of the respondents only augment their decision making process in less than 50% of their decisions with (alternative) human opinions.
Obviously, when decision-makers believe they are better in making decisions than their peers, there might not be such a great incentive for seeking alternative human opinions to what a decision-maker has already concluded to be the best way forward based on available data (or “gut instinct”).
The question is whether A.I.-augmented decision making could be a game changer in how corporations make decisions? Will an algorithmic data-driven approach provide the framework for better and more valuable decisions than is the case today, where largely human-driven decision making, with all its cognitive biases, rules. Will silicon-based decision making overtake biology-based decision making and will such decisions be better?
How does the human corporate decision maker perceive artificial intelligence? Is A.I. perceived as threat? As an opportunity? or a bit of both?
As Tim O’Reilly might say WTF? or my grandmother WTF!
THOSE A.I. “SUCKERS” ARE NOT GOING TO MESS WITH MY DECISIONS!?
Firstly, the survey revealed that, not surprising, most of the respondents had heard about Artificial Intelligence prior to the survey. In this survey a little more than 90% of the respondents had heard of A.I..
In the following, respondents that have not heard of A.I. have been filtered out of the analysis. This filtering is in addition to the filtering out respondents providing “Retired” as job level. In total 39 respondents (6.4%) had not heard of A.I. at the time of the survey. This leaves a remaining sample of 569 out of the original 658 (i.e., of which 50 was retired and an additional 39 had not heard of A.I. prior to this Survey).
So … How do we feel about A.I.?
The average sentiment and standard deviation (in the parenthesis) across all respondent (i.e., who have heard of A.I.) was 2.65 (0.88). An average score that would indicate a sentiment between “I am neutral” and “I am very comfortable with it”.
The survey did reveal a statistically significant gender difference in the sentiment towards A.I.. Women (i.e., 2.82 (0.85)) sentiment towards A.I. is more neutral than mens (i.e., 2.57 (0.88)). This is also reflected in proportionally more women indicating more negative sentiments (i.e., “I am uncomfortable with it” or “I hate it”).
If we ignore the neutral category of 46%, which might swing to either side pending on future information and experience, there are 41% of the respondents who have a very positive sentiment towards A.I. (i.e., either “very comfortable” with or “love” A.I.). Only 13% of the respondents express unease (11%) or direct hate (2%) against A.I.. Also here gender difference is observed. 17% of women expressed concern about A.I. compared to 12% of men.
Many more people are positive towards A.I. than negative.
Women seam on average to be slightly more reserved towards A.I. than men. Although significantly more women than men have stronger reservations against A.I..
From a job-level perspective, “Owner/Executive/C-Level” have the 2nd most positive average attitude towards A.I. followed by “Senior Management”. However, the lowest standard variation is found for “Senior Management” which might indicate a higher degree of conformity towards A.I. than found in any other job-level categories including the “Owner/Executive/C-Level” category. What is interesting and at least for me not self-explanatory is that the “Entry level” category appears to have the most positive attitude towards A.I., a difference that is statistically significant within a 95% confidence. This aspect will be further explored in an upcoming analysis.
From an education perspective, respondents with a graduate-level degree are more positive in their attitude towards A.I. than for example 4-year college degree respondents or respondents with some college education but no degree. These findings a likewise statistically significant within a 95% confidence. Difference between other categories are apparent (e.g., mean score systematically worsen with less education) however distribution wise not statistically significant (within a 95% confidence level).
The higher the educational level of people the more positive is A.I. viewed.
Furthermore, the higher the educational level the less likely are people to have stronger reservations against A.I..
I wanted to check whether there might be any difference towards a respondents answer depending on emphasizing that the A.I. is a “decision-making optimized” A.I. (i.e., the B-variant) or just kept the question general without the emphasis on the A.I. having been optimized for decision-making (i.e, the A-variant).
Questions 19 to 24 are run as an A/B testing. The intention is to check whether there is a difference in a respondent’s answer based A-variant “an A.I.” or B-variant “a decision-making optimized A.I.”. Approximately 50% of respondents got the A-variant and the remainder (i.e., ca. 50%) got the B-variant.
In the following, I will present the responses as consolidated view of both the A- and B-variants, if there is no statistical difference between the A- and B-distributions within 95% confidence.
Imagine an A.I. would be available to your company. It might even be a decision-making optimized A.I., trained to your corporate decision-making history (btw it would be reasonably useless to you if it wasn’t trained on relevant data) as well as public available data on decisions outcomes. It might even have some fancy econometric and psychometric logic that test decision space for rationality and cognitive-biases of proposed decisions. Such a tool will not only be able to tell you whether your proposed decision is sound but also provide you with recommendations of better decisions and how your competitors might respond.
Thus, instead of being fairly mono-dimensional in considerations around a given corporate decision, this A.I. will provide a holistic picture (or scenarios) of a given decisions most likely impact on the future; the value short, medium and long term, competitive responses, etc..
Would that not be great … ?
Within 95% confidence there is no statistical difference between the distribution of A answers and B answers. Thus I have chosen to show both together. At a deeper level, e.g., job-level, age, or other responder characteristics, there are also in general no statistical difference between A-variant and B-variant distributions.
Almost 30% of the respondents believe that an A.I. would be un-important or irrelevant to their company’s decision-making process. About the same percentage (i.e., 32%) believe that it will be very important (30%) or always used (2%). About 40% expects it to be used in approximately half of all decisions. The last part would obviously be a quantum-leap in A.I. adaptation compared to today where that number is very low.
1 in 3 decision makers to not expect A.I. to become important in their corporate decisions.
Senior Management are more optimistic towards the importance of A.I. in the corporate decision making process compared to their leadership (i.e., the “Owner / Executive / C-level” category). Although Middle Management are statistically less inclined. Again it is found that “Entry Level” respondents are more bullish towards A.I. than higher management.
More than 70% of corporate decision makers believe that A.I. will be important in 50% or more of a companies decisions.
Upper Management and Entry Level respondents are more strongly believing in the A.I. adaptation in the corporate decision making process.
Okay! but would you trust a decision based on an A.I. consultation? This of course could involve a human decision makers decision augmented by an A.I. consultation rather than a more human driven decision making process.
Only 3% of the respondents would always trust a decision based on A.I. consultation. 27% frequently and 40% only in about half the time that a decision was based on A.I. consultation (i.e., might as well flip a coin).
About 30% of the respondents would infrequently or never trust a decision based on A.I. consultation.
Before pondering on the job-level dependency, note that there is no statistical difference between A and B answer distributions. This also holds true in general on deeper respondent level.
Does any one particular group have A.I. trust issues? … hmmm
Clearly, the “Owner/Executive/C-level” respondent category, which is a pretty important category in a company’s decision-making process, really seem to have the greatest degree of trust issues towards A.I.. 31% of “Owner/Executive/C-level” would never or infrequently trust a decision based on A.I. consultation. For me this is a wow! and if in general true for corporations might signal some barriers towards wide adaptation of A.I.’s in companies decision making processes.
However, it is also fair to note that the “Executive” category also has the largest variance in response across the trust-scale used here compared to any of the other job-level categories.
As is a recurrent theme. Upper management and Entry-level managers are overwhelmingly (and significantly) trusting towards decisions based on A.I. consultation compared to their colleagues in other management categories.
Owners, Executives and C-level decision makers are substantially (and significantly) less trusting towards decisions made in consultation with an A.I..
Current verdict seems to be that corporate decision makers don’t really trust the “suckers” (=A.I.).
So the next one should be easy. How often would you follow a human advise different from your A.I.’s recommendation? (in retrospect I really should also have asked how often a respondent would follow an A.I.’s advice different from respondents own opinion … but alas for an up and coming survey).
About 50% of respondent it appears would prefer “to flip a coin” to determine whether to follow the human advice or the to go ahead with the A.I. recommendation. Okay … this is of course not what is asked and maybe also not what is meant … However, if you in half of A.I. recommended decisions being disputed by a follow human the decision maker, follow either one or the other … then they might as well flip a coin.
It is maybe good to re-remind the reader that algorithmic approaches perform in general better than human-based decisions or on the downside at least as well.
ca. 30% of our decision makers would follow the human advice rather than continue with the A.I. recommendation. Less than 20% would be relative bold and go ahead with an A.I. recommendation disputed by a fellow human.
Lets just ask again … Does any particular job-level have trust issues?
20% of the “Owner/Executive/C-level” respondent would only infrequently follow a fellow Humans advice different from an A.I. recommendation. Note in Question 20 (above) 33% of the Executives (i.e., “Owner/Executive/C-level”) would trust an A.I. based consultation with 28% frequently. This appears completely consistent as A.I. recommendations subject to Human dispute would result in a reduction of such being pursued.
Irrespective, the majority is in the “flip a coin category” which might mean that they neither trust the A.I. or the Human … this will be more through roughly pursued in a follow up analysis going deeper into the data available and in more refined surveys.
Assume you have an A.I. available to consult and to guide your decisions. It is an integral (or maybe not so integral?) part of your company’s data-driven decision-making process. How often would you follow such an A.I.’s advice?
Remember that 40% of the respondents would trust a decision based on A.I. consultation about half the time (i.e., what the cynic might call “coin flip strategy”). 27% would trust such a decision frequently and 24% infrequently.
Would you follow an advice based on something you doubt? Well the result of Question 22 could to an extend be interpreted in this way. 31% of respondents would frequently follow the A.I. advice which is only marginally higher than the 27% that frequently would trust a decision based on A.I. consultation (i.e., Question 20). 46% would follow the A.I. advice in about half the time they are in such a situation. Finally 16% would follow the advice infrequently although 24% of the respondents only infrequently would trust a decision based on A.I. consultation. There is a difference between following an advice and trusting it as history also teaches us I suppose.
More decision makers would follow an A.I. advice than trust decisions based on A.I. consultation.
From a job-level perspective the response are reasonably consistent with the previous two questions addressed above,
Consistent in the sense that irrespective of the above specific trust in decisions based on A.I. consultation, respondents would still go ahead an follow an advice based on A.I..
Coming to the end of this survey, it is fair to ask the question whether a company with an A.I. available for its corporate decision-making process would actually need the decision maker.
So … are you needed you think? … Yes, after the results of the above Questions 19 – 23 it does become a bit rhetoric …
So … 65% of the decision makers believe that their decsion-making skills will remain needed by their companies. 24% expects that in about half of the time their skills would still be needed and 10% expects it to be infrequent or never.
There is no statistically significant differences between job-levels in their answer to this questions.
There is a strong sense among decision makers that their decision making skills will continue to be required by their companies irrespective for A.I.’s being available to their companies decision-making processes.
I greatly acknowledge my wife Eva Varadi for her support, patience and understanding during the creative process of creating this Blog. Without her support, I really would not be able to do this or it would take long past my expiration date to finish.
Barry Libert & Megan Beck, “AI May Soon Replace Even the Most Elite Consultants” , Harvard Business Review (July 2017). If an Elite consultant can be replaced by Alexa (Amazon’s A.I.) or another A.I.-bot that basically is a Wikipedia with a voice, then obviously that consultant should be replaced. But maybe more importantly the CxO wasting money on an Elite Consultant acting as an biological wikipedia maybe more so (imho).
Ajay Agrawal, Joshua Gans & Avi Goldfarb, “How AI Will Change the Way We Make Decisions.”, Harvard Business Review (July 2017). The devil is in the detail and not all corporate decisions would easily be taken over by A.I. (e..g, decisions that are based on tiny amounts of data). However it really is a trade-off of how much human error / risk can you tolerate versus an A.I. error (e.g., false positives, false negatives, ..) on various types of decisions.
Berkeley J. Dietvorst, Joseph P. Simonojs and Cade Massey, “Algorithm Aversion: people erroneously avoid algorithms after seeing them err.”, Journal of Experimental Psychology: General (2014). Study on the wide spread Algorithm Aversion, i.e., human expectations towards machines are substantially higher than to fellow humans. This results in a irrational aversion of machine based recommendations versus human-based recommendation. Even though algorithmic based forecasts are on average better to much better than human based equivalent in apples by apples comparisons.
Amanda Levendowski, “How copyright law can fix artificial intelligence’s implicit bias problem“, Washington Law Review, forthcoming. Latest review 14 October 2017. Latest draft version can be downloaded from the url link provided. The draft paper provides an authoritative account for the issues around biases arising from training A.I. on available datasets (in private as well as public domain). Also some interesting ideas how copyright might mitigate some of the A.I. bias risks we certainly see in todays implementations.
Robert Sapolsky’s “Behave: the biology of Humans at our best and worst” , Pinguin Random House UK (2017). Robert has been my companion throughout the summer and fall. I have read his book a couple of times and have it in its Audible version as well. It is not only insanely entertaining but also very thought provoking as it relates to our behavior and why we humans at times are so bad decision makers.
Tim Swanson, “Great Chain of Numbers”, (2014). Providing an excellent overview of what is already possible to day with smartcontracts and blockchain enabled DAOs (i.e., Distributed Autonomeous Organizations) and so forth. Obviously, also shows what the future could look like.
Antonio R. Damasio, “Descartes Error: Emotion, Reason and the Human Brain”, Avon Books (1994). This is a very interesting account for human emotions, reason and decision making and how our brain supports and messes the whole thing up. In order to appreciate Damasio’s work it is important to understand the distinction between Emotions (what an 3rd party observer can see) and Feelings (what an individual senses). I am likely at fault of occasionally mixing up the two concepts.
Barneby B. Dunn, Tim Dalgleish and Andrew D. Lawrence, “The somatic marker hypothesis: a critical evaluation”, Neuroscience and Biobehavioral Reviews (2005) 1 – 33. Antonio Damasio’s somatic marker hypothesis, from around 1991, has been (and remain) very influential as an explanation of Brain – Body coupling or feedback. Albeit the idea is not scientifically proven in all its aspects and often is prone to various interpretations. You will in this paper find a comprehensive reference list to the most important literature in this field.
and yes new responses will be collected under a separate Collector Group.
The questionnaire consist of 24 questions roughly structure as
General information about you.
Your corporate decision making skills.
The quality of data used in your decision-making process.
Your acceptance of A.I. as it related to your corporate decision-making processes.
The typical time spent on answering the 24 questions is a bit less than 4 minutes.
Q1 – What is your gender?
Q2 – What is your age?
17 or younger.
18 – 20.
21 – 29.
30 – 39.
40 – 49.
50 – 59.
60 or older.
Q3 – What is the highest level of school that you have completed?
Some high school, but no diploma.
High school diploma (or GED).
Some college, but no degree.
2-year college degree.
4-year college degree.
None of the above.
Q4 – Which of the following best describes your current job level?
Other (please specify).
Q5 – What department do you work in?
Research & Development.
Q6 – Do you enjoy making decisions relevant to your company?
I hate making decisions.
I do not enjoy making decisions.
I am okay with making decisions.
I enjoy making decisions.
I love making decisions.
Q7 – How would you characterize your decision making skills in comparison with your peers?
Q8 – Do you consult with others before making a decision?
I rarely consult others (e.g., 3 our of 10 times or lower).
Approximately half of my decisions have been consulted with others.
I frequently consult others (e.g., 7 out of 10 times or higher).
Q9 – Do you rely on “gut feelings” when making corporate decisions?
Approximately half of my decisions.
Q10 – How would you characterize your “gut feelings” compared to your peers?
Q11 – How often is available data considered in your corporate decisions?
Data is never considered.
For approximately half of my decisions data is considered.
Data is always considered.
Q12 – How often is data available for your corporate decisions?
Never or very rarely.
For approximately half of my decisions.
Very frequently or always.
Q13 – When data is available, how would you characterize the quality of that data?
Very poor (i.e., no basis for decisions).
Poor (i.e., uncertain, error prone, biased, very limited data available).
Good (i.e., uncertain but can be relied upon, some bias, limited data available).
High (i.e., reliable, sizable data available, limited uncertainty).
Very high (i.e., meets the stringiest test to data quality, large amounts of data).
Q14 – How would you characterize your most important decisions in terms of the use of available & relevant data?
Never data-driven (i.e., no relevant data available).
Rare-data driven (i.e., tiny amount of relevant data available).
Small-data driven (i.e., little relevant data available).
Large-data driven (i.e., large amounts of relevant data available).
Big-data driven (i.e., huge amount of relevant data available).
Q15 – How important is human opinion compared to data-driven insights in your decision making?
It is irrelevant.
It is of some importance.
About half of my decisions are based on human insights.
It is very important.
It is exclusively used for my decisions.
Q16 – How would you characterize the quality of the data available to you and used in important corporate decisions compared to your competition?
About the same.
Q17 – Have you heard of Artificial Intelligence (A.I.)?
Q18 – How would you best describe your feelings toward A.I.?
I love it.
I am very comfortable with it.
I am neutral.
I am uncomfortable with it.
I hate it.
The following questions are broken into an A and a B part. Approximately 50% of respondents will be presented with either A or B. I am in particular interested in understanding whether respondents changes their sentiment to A.I., whether the question is neutral towards A.I. (A-path) or specifically mentions that the A.I. is decision-making optimized (the B-path).
Q19A (~ 50%) – If an A.I. would be available to your company, how important do you think it would be in your company’s decision making processes?
Important in about half of all decisions.
Q19B (~50%) – If a decision-making optimized A.I. would be available to your company, how important do you think it would be in your company’s decision making processes?
Important in about half of all decisions.
Q20A (~50%) – Would you trust a decision based on A.I. consultation?
About half the time.
Q20B (~50%) – Would you trust a decision based on a decision-making optimized A.I. consultation?
About half the time.
Q21A (~50%) – If an A.I. would be available to you, how frequently do you think this A.I. would be consulted in your decision making process?
About half the time.
Q21B (~50%) – If a decision-making optimized A.I. would be available to you, how frequently do you think this A.I. would be consulted in your decision making process?
About half the time.
Q22A (~50%) – If an A.I. would be available to guide your decisions, how often would you follow its advice?
About half the time.
Q22B (~50%) – If a decision-making optimized A.I. would be available to guide your decisions, how often would you follow its advice?
About half the time.
Q23A (~50%) – If an A.I. would be available to guide your decisions, how often would you follow Human advices different from your A.I.’s recommendation?
About half the time.
Q23B (~50%) – If a decision-making optimized A.I. would be available to guide your decisions, how often would you follow Human advices different from your A.I.’s recommendation?
About half the time.
Q24A (~50%) – If an A.I. would be available to your company, do you think your company still would need your decision making skills?
About half the time.
Q24B (~50%) – If a decision-making optimized A.I. would be available to your company, do you think your company still would need your decision making skills?
I am currently conducting some research into corporate decision making, as referenced to the individual corporate decision maker, and the extent and perceived quality of data-driven decisions in the corporate decision making process.
In this (initial) research, it is of particular interest to understand the extend of corporate data-driven decision making. The initial part of the survey focuses on (1) type of corporate decisions made, (2) the availability of data used in the decision making, (3) the structure of such data, (4) the quality of such available data and so forth.
The second part of the Survey attempts to gauge human expectations towards and acceptance of artificial intelligence (A.I.) in corporate decision making as it relates to the individual decision maker and the overall corporate decision making process.
The (bold blue text) link below takes you to the Survey with 24 questions which should take less than 4 minutes (current average is 3 minutes 31 seconds);
SELRES_774c01bf-c5c4-4ebc-a7f3-fc6234834b82SELRES_7e79026e-dc05-4473-a56c-017ee2665ff8The survey results will be published in an essay I am currently writing on “Corporate Decision Making – SELRES_7e79026e-dc05-4473-a56c-017ee2665ff8SELRES_774c01bf-c5c4-4ebc-a7f3-fc6234834b82The neuroscience of human decisions and the role that Artificial Intelligence may play” (working title!;-). Some of the thoughts and analysis will also appear here on this Blog … so stay tuned.
The impact of A.I. is widely discussed and reasonably understood in the context of consumerism, product development and the harder science (i.e., at least the “hows”, maybe not always the “whys”).
Little research work has been done on how Artificial Intelligence may shape businesses and their corporate decision making processes. Certainly, very little is known about the sentiment of management and executives towards A.I. in context of decision making and the possibility of augmenting such with A.I.-based insights. The impact of A.I. augmentation on the individual corporate decision maker, as well as for the business in its totality, is likely to be transformative.
So far I have collected survey data from 550+ executives and managers.
If you have taken the survey then very much appreciated and thanks (please don’t take it again;-). If you have not yet, please click the above link … it should not take more than maximum 4 minutes of your life.
I expect to publish the first results of this Survey by early October.