• February 8, 2023

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Sometimes you are on the wrong end of the stick.

That colloquialism can be applied to the notion of asymmetry.

Yes, I am going to be talking about asymmetry. As you likely have encountered in this topsy-turvy world that we live in, there are occasions when you might find yourself having less knowledge on a matter that is relatively important to you. This is formally referred to as Information Asymmetry.

The key is that you have less knowledge or information than you might wish that you had, plus you decidedly have less than the other party involved in the matter. You are at a distinct disadvantage in comparison to the other party. They know something you don’t know. They can leverage what they know, especially in terms of what you don’t know, and get an upper hand in any rough-and-tumble deliberations or negotiations with you.

Well, there is a new kid in town, known as AI Asymmetry.

This latest catchphrase refers to the possibility of you going against someone that is armed with AI, while you are not so armed.

They have AI on their side, while you’ve got, well, just you. Things are lopsided. You are at a presumed disadvantage. The other side will be able to run circles around you due to being augmented by AI. That might be within the famous saying that all is fair in love and war (a longstanding proverb coined in Euphues by John Lyly, 1578), though the dynamics and dangers of AI Asymmetry raise challenging Ethical AI issues. For my ongoing and extensive coverage of AI Ethics and Ethical AI, see the link here and the link here, just to name a few.

Before we jump into the AI realm and its abundant complexities regarding AI Asymmetry, let’s first explore the everyday regular version of plain old Information Asymmetry. This will set the stage for edging into the proverbial AI new kid on the block.

A brief and purposefully enlightening tale might whet your appetite.

The other day I had a flat tire while on the road and was seeking quickly to find a suitable replacement that could be readily installed right away. Using my smartphone, I looked online at nearby tire stores to figure out the distance I had to drive on my run-flat tire and whether any stores were open. In addition, I did a quick assessment of their online customer reviews and tried to glean anything useful about how long they had been in business and other factors that might showcase their worthiness.

Upon calling one of the tire stores, the clerk gave me a breezy quote for the cost of the tire and its installation. The tire was not exactly what I had in mind, but the clerk assured me that they would be the only shop in the area that could do the work straight away. According to the clerk, any of the other nearby tire stores would not have any such tires in stock and it would take at least a day for those competitors to obtain a suitable tire from some semi-distant warehouse.

I was in the midst of an information asymmetry.

The clerk professed to know more about the local status of the tire stores and in particular the type of tire that I needed. I was in an area that I was only passing through and didn’t have any first-hand knowledge about the tire shops in that particular geographical area. For all I knew, the clerk was spot on and was giving me the unvarnished truth.

But was the clerk doing so?

Maybe yes, maybe no.

It could be that the clerk believed sincerely everything that was being conveyed to me. To the clerk, this was the truth. Or perhaps the clerk was somewhat stretching the truth. It was possible that what was being said could possibly be true, though the manner in which it was being depicted implied that it was the utter and irrefutable truth. Of course, it could also have been complete balderdash and the clerk was merely shilling for the tire store to garner my business. Could a juicy commission have been on the line?

I dare say that nobody likes being in such an underdog position.

The stakes of the situation are a vital factor in how much an information asymmetry matters. If the question at hand is one of a life-or-death nature, being in the doghouse and reliant upon the other party for what they know or profess to know is a sketchy and highly undesirable posture to be in. When the stakes are low, such as ordering your dinner in a restaurant and the server tells you that the fish dish is heavenly, but you’ve never eaten there before and are under-informed, you can go along with this modicum of information asymmetry without much angst (I suppose too that you are also betting that the server wouldn’t risk giving sour advice and missing out on getting a decent tip).

Returning to the worn-out tire story (a pun!), I would have had no instantaneous way to figure out whether the clerk was giving me reliable and informative insights. You might be wondering what happened. I decided to make calls to several of the other nearby tire stores.

Are you ready for what I discovered?

All the other tire stores had my desired tire in stock and weren’t going to try and wink-wink persuade me to take a different tire (as the first clerk tried to do). They also could get the work done in the same timeframe as the first tire store that I perchance called. At roughly the same price.

A welcomed sigh of relief occurred on my part, I assure you.

Ironically, in Murphy’s Law of bad luck, the first place that I contacted was the only one that seemed to be out to lunch, as it were. I’m glad that I sought to obtain more information. This narrowed the information asymmetry gap. I applauded myself for having stuck to my guns and not acceding to the first place I called.

That being said, there was a definite cost of sorts involved in my obtaining additional information. I made approximately four calls that each took around fifteen to twenty minutes to fully undertake. In that sense, I used up about an hour and a half while just figuring out where to take my car. If I had immediately taken my car to that first place, the new tire would nearly have been on my car by that time. On the other hand, I would almost certainly, later on, have regretted the quick decision that I made while in a dastardly Information Asymmetry bind.

Sometimes you have to grit your teeth and take the dreaded Information Asymmetry as it comes. You just hope that whatever decision you make, is going to be good enough. It might not be a “perfect” decision and you could later regret the choice made. The other angle is that you could try to bolster your side of the information equation, though this is not necessarily cost-free and might also chew up precious time, depending upon whether the cherished time is of the essence.

Now that you are undoubtedly comforted to know that my car is running fine with its brand new and correct tire, I can shift into the emergence of AI Asymmetry.

Consider an AI tale of woe.

You are seeking to get a home loan. There is an online mortgage request analyzer that a particular bank is using. The online system makes use of today’s advanced AI capabilities. No need to speak with a human loan granting agent. The AI does it all.

The AI system walks you through a series of prompts. You dutifully fill in the forms and respond to the AI system. This AI is very chatty. Whereas you in the past might have used a conventional computer-based form system, this AI variant is more akin to interacting with a human agent. Not quite, but enough that you could almost start to believe that a human was on the other side of this activity.

After doing your best to “discuss” your request with this AI, in the end, it informs you that unfortunately the loan request is not approved. It kind of gets your goat that the AI seems to offer an apology, as though the AI wanted to approve the loan but those mean-spirited humans overseeing the bank won’t let the AI do so. For my coverage of how misleading these kinds of alleged AI apologies are, see the link here.

You are clueless as to why you got turned down. The AI doesn’t proffer any explanation. Perhaps the AI made a mistake or messed up in its calculations. Worse still, suppose the AI used some highly questionable considerations such as your race or gender when deciding on the loan. All that you know is that you seemed to have wasted your time and also meanwhile handed over a ton of private data to the AI and the bank. Their AI has bested you.

This would be labeled as an example of AI Asymmetry.

It was you against the bank. The bank was armed with AI. You were not equally armed. You had your wits and your school of hard knocks wisdom, but no AI residing in your back pocket. Mind against a machine. Sadly, the machine won in this case.

What are you to do?

First, we need on a societal basis to realize that this AI Asymmetry is growing and becoming nearly ubiquitous. Humans are encountering AI in all of the systems that we daily interact with. Sometimes the AI is the only element that we interact with, such as in this example about the loan request. In other instances, a human might be in the loop that relies upon AI to aid them in performing a given service. For the loan, it might be that the bank would have you speak with a human agent in lieu of interacting with AI, but for which the human agent is using a computer system to access AI that is guiding the human agent during the loan request process (and, you are nearly always assured to have the human agent act as though they are imprisoned by having to strictly do whatever the AI “tells them to do”).

Either way, AI is still in the mix.

Second, we need to try and ensure that the AI Asymmetry is at least being done on an AI Ethical basis.

Allow me to explain that seemingly oddish remark. You see, if we can be somewhat assured that the AI is acting in an ethically sound manner, we might have some solace about the asymmetry that is at play. On a somewhat analogous but also loose basis, you might say that if my interaction with the first tire store clerk had some strident ethical guidelines in place and enforced, perhaps I would not have been told the story that I was told, or at least I might not have had to right away seek to discover whether a tall tale was being given to me.

I’ll be explaining more about AI Ethics in a moment.

Third, we should seek ways to reduce AI Asymmetry. If you had AI that was on your side, striving to be your coach or protector, you might be able to use that AI to do some counterpunching with the other AI that you are going head-to-head with. As they say, sometimes it makes abundant sense to fight fire with fire.

Before getting into some more meat and potatoes about the wild and woolly considerations underlying AI Asymmetry, let’s establish some additional fundamentals on profoundly essential topics. We need to briefly take a breezy dive into AI Ethics and especially the advent of Machine Learning (ML) and Deep Learning (DL).

You might be vaguely aware that one of the loudest voices these days in the AI field and even outside the field of AI consists of clamoring for a greater semblance of Ethical AI. Let’s take a look at what it means to refer to AI Ethics and Ethical AI. On top of that, we will explore what I mean when I speak of Machine Learning and Deep Learning.

One particular segment or portion of AI Ethics that has been getting a lot of media attention consists of AI that exhibits untoward biases and inequities. You might be aware that when the latest era of AI got underway there was a huge burst of enthusiasm for what some now call AI For Good. Unfortunately, on the heels of that gushing excitement, we began to witness AI For Bad. For example, various AI-based facial recognition systems have been revealed as containing racial biases and gender biases, which I’ve discussed at the link here.

Efforts to fight back against AI For Bad are actively underway. Besides vociferous legal pursuits of reining in the wrongdoing, there is also a substantive push toward embracing AI Ethics to righten the AI vileness. The notion is that we ought to adopt and endorse key Ethical AI principles for the development and fielding of AI doing so to undercut the AI For Bad and simultaneously heralding and promoting the preferable AI For Good.

On a related notion, I am an advocate of trying to use AI as part of the solution to AI woes, fighting fire with fire in that manner of thinking. We might for example embed Ethical AI components into an AI system that will monitor how the rest of the AI is doing things and thus potentially catch in real-time any discriminatory efforts, see my discussion at the link here. We could also have a separate AI system that acts as a type of AI Ethics monitor. The AI system serves as an overseer to track and detect when another AI is going into the unethical abyss (see my analysis of such capabilities at the link here).

In a moment, I’ll share with you some overarching principles underlying AI Ethics. There are lots of these kinds of lists floating around here and there. You could say that there isn’t as yet a singular list of universal appeal and concurrence. That’s the unfortunate news. The good news is that at least there are readily available AI Ethics lists and they tend to be quite similar. All told, this suggests that by a form of reasoned convergence of sorts that we are finding our way toward a general commonality of what AI Ethics consists of.

First, let’s cover briefly some of the overall Ethical AI precepts to illustrate what ought to be a vital consideration for anyone crafting, fielding, or using AI.

For example, as stated by the Vatican in the Rome Call For AI Ethics and as I’ve covered in-depth at the link here, these are their identified six primary AI ethics principles:

  • Transparency: In principle, AI systems must be explainable
  • Inclusion: The needs of all human beings must be taken into consideration so that everyone can benefit, and all individuals can be offered the best possible conditions to express themselves and develop
  • Responsibility: Those who design and deploy the use of AI must proceed with responsibility and transparency
  • Impartiality: Do not create or act according to bias, thus safeguarding fairness and human dignity
  • Reliability: AI systems must be able to work reliably
  • Security and privacy: AI systems must work securely and respect the privacy of users.

As stated by the U.S. Department of Defense (DoD) in their Ethical Principles For The Use Of Artificial Intelligence and as I’ve covered in-depth at the link here, these are their six primary AI ethics principles:

  • Responsible: DoD personnel will exercise appropriate levels of judgment and care while remaining responsible for the development, deployment, and use of AI capabilities.
  • Equitable: The Department will take deliberate steps to minimize unintended bias in AI capabilities.
  • Traceable: The Department’s AI capabilities will be developed and deployed such that relevant personnel possesses an appropriate understanding of the technology, development processes, and operational methods applicable to AI capabilities, including transparent and auditable methodologies, data sources, and design procedure and documentation.
  • Reliable: The Department’s AI capabilities will have explicit, well-defined uses, and the safety, security, and effectiveness of such capabilities will be subject to testing and assurance within those defined uses across their entire lifecycles.
  • Governable: The Department will design and engineer AI capabilities to fulfill their intended functions while possessing the ability to detect and avoid unintended consequences, and the ability to disengage or deactivate deployed systems that demonstrate unintended behavior.

I’ve also discussed various collective analyses of AI ethics principles, including having covered a set devised by researchers that examined and condensed the essence of numerous national and international AI ethics tenets in a paper entitled “The Global Landscape Of AI Ethics Guidelines” (published in Nature), and that my coverage explores at the link here, which led to this keystone list:

  • Transparency
  • Justice & Fairness
  • Non-Maleficence
  • Responsibility
  • Privacy
  • Beneficence
  • Freedom & Autonomy
  • Trust
  • Sustainability
  • Dignity
  • Solidarity

As you might directly guess, trying to pin down the specifics underlying these principles can be extremely hard to do. Even more so, the effort to turn those broad principles into something entirely tangible and detailed enough to be used when crafting AI systems is also a tough nut to crack. It is easy to overall do some handwaving about what AI Ethics precepts are and how they should be generally observed, while it is a much more complicated situation in the AI coding having to be the veritable rubber that meets the road.

The AI Ethics principles are to be utilized by AI developers, along with those that manage AI development efforts, and even those that ultimately field and perform upkeep on AI systems. All stakeholders throughout the entire AI life cycle of development and usage are considered within the scope of abiding by the being-established norms of Ethical AI. This is an important highlight since the usual assumption is that “only coders” or those that program the AI is subject to adhering to the AI Ethics notions. As earlier stated, it takes a village to devise and field AI, and for which the entire village has to be versed in and abide by AI Ethics precepts.

Let’s also make sure we are on the same page about the nature of today’s AI.

There isn’t any AI today that is sentient. We don’t have this. We don’t know if sentient AI will be possible. Nobody can aptly predict whether we will attain sentient AI, nor whether sentient AI will somehow miraculously spontaneously arise in a form of computational cognitive supernova (usually referred to as the singularity, see my coverage at the link here).

The type of AI that I am focusing on consists of the non-sentient AI that we have today. If we wanted to wildly speculate about sentient AI, this discussion could go in a radically different direction. A sentient AI would supposedly be of human quality. You would need to consider that the sentient AI is the cognitive equivalent of a human. More so, since some speculate we might have super-intelligent AI, it is conceivable that such AI could end up being smarter than humans (for my exploration of super-intelligent AI as a possibility, see the coverage here).


Let’s keep things more down to earth and consider today’s computational non-sentient AI.

Realize that today’s AI is not able to “think” in any fashion on par with human thinking. When you interact with Alexa or Siri, the conversational capacities might seem akin to human capacities, but the reality is that it is computational and lacks human cognition. The latest era of AI has made extensive use of Machine Learning (ML) and Deep Learning (DL), which leverage computational pattern matching. This has led to AI systems that have the appearance of human-like proclivities. Meanwhile, there isn’t any AI today that has a semblance of common sense and nor has any of the cognitive wonderment of robust human thinking.

ML/DL is a form of computational pattern matching. The usual approach is that you assemble data about a decision-making task. You feed the data into the ML/DL computer models. Those models seek to find mathematical patterns. After finding such patterns, if so found, the AI system then will use those patterns when encountering new data. Upon the presentation of new data, the patterns based on the “old” or historical data are applied to render a current decision.

I think you can guess where this is heading. If humans that have been making the patterned upon decisions have been incorporating untoward biases, the odds are that the data reflects this in subtle but significant ways. Machine Learning or Deep Learning computational pattern matching will simply try to mathematically mimic the data accordingly. There is no semblance of common sense or other sentient aspects of AI-crafted modeling per se.

Furthermore, the AI developers might not realize what is going on either. The arcane mathematics in the ML/DL might make it difficult to ferret out the now hidden biases. You would rightfully hope and expect that the AI developers would test for the potentially buried biases, though this is trickier than it might seem. A solid chance exists that even with relatively extensive testing that there will be biases still embedded within the pattern matching models of the ML/DL.

You could somewhat use the famous or infamous adage of garbage-in garbage-out. The thing is, this is more akin to biases-in that insidiously get infused as biases submerged within the AI. The algorithm decision-making (ADM) of AI axiomatically becomes laden with inequities.

Not good.

Let’s return to our focus on AI Asymmetry.

A quick recap about my aforementioned three identified recommendations is this:

1) Become aware that AI Asymmetry exists and is growing

2) Seek to ensure that the AI Asymmetry is bounded by AI Ethics

3) Try to contend with AI Asymmetry by getting armed with AI

We will take a closer look at the latter point of fighting fire with fire.

Imagine that when seeking to get a loan, you had AI that was working on your side of the effort. This might be an AI-based app on your smartphone that was devised for getting loans. It isn’t an app by one of the banks and instead is independently devised to act on your behalf. I’ve detailed these kinds of apps in my book on AI-based guardian angel bots, see the link here.

Upon your applying for a loan, you might refer to this app as you are stepped through the application process by the other AI. These two AI systems are distinct and completely separate from each other. The AI on your smartphone has been “trained” to know all of the tricks being used by the other AI. As such, the answers that you enter into the bank’s AI will be based on what your AI is advising you.

Another variant consists of your AI answering the questions posed by the other AI. As far as the other AI can ascertain, it is you that are entering the answers. You might instead be merely watching as the interactions take place between the two battling AI systems. This allows you to see what your AI is proffering. Furthermore, you can potentially adjust your AI depending upon whether you are satisfied with what your AI is doing on your behalf.

I have predicted that we are all going to gradually become armed with AI that will be on our side in these AI Asymmetry situations.

Let’s consider how this is going to work out.

These are the cornerstone impacts on the AI Asymmetry condition that I had laid out:

  • Flattening the AI Asymmetry in your favor (bringing you upward, hoping to reach equal levels)
  • Spurring an AI Asymmetry to your favor (raising you to an advantage when already are equals)
  • Boosting an AI Asymmetry to your extraordinary favor (gaining a wider advantage when already at an advantage)
  • Inadvertent Undercutting of AI Asymmetry to your disfavor (when you had a preexisting advantage and the AI inadvertently pulled you down)

Time to do a deep dive into these intriguing possibilities.

Flattening The AI Asymmetry In Your Favor

Flattening the AI Asymmetry is the most obvious and most often discussed consideration, namely that you would arm yourself with AI to try and go toe-to-toe with the AI being used by the other side in the matter at hand. The AI Asymmetry setting started with you at a decided disadvantage. You had no AI in your corner. You were on the low side of things. The other side did have AI and they were on the higher ground.

Thus, you wisely armed yourself with AI that would aim to put you and the other AI on equal terms.

One important and perhaps surprising nuance to keep in mind is that it won’t always be the case that the AI systems being employed will balance against each other evenly. You might arm yourself with AI that is shall we say less potent than the AI that the other side is using. In which case, you have increased your downside position, thankfully, though you are not entirely now equal with the other side and its AI.

That’s why I refer to this as flattening the AI Asymmetry. You might be able to narrow the gap, though not fully close the gap. The ultimate aim would be to use AI on your side that will bring you to a completely equal posture. The thing is, this might or might not be feasible. The other side could conceivably have some really expensive AI and you are trying to compete with the mom-and-pop thrifty mart version of AI.

Not all AI is the same.

Spurring An AI Asymmetry To Your Favor

This circumstance is not something much discussed today, partially because it is rare right now. Someday, this will be commonplace. The notion is that suppose you are without AI and yet nonetheless on equal ground with the side that does have AI.

Good for you.

Humans do have their wits about them.

But you might want to gain an advantage over the other side. Arming yourself with AI takes you to the higher ground. You now have your wits and your trusty AI in hand. You have gained an advantage that presumably will prevail over the AI of the other side.

Boosting An AI Asymmetry To Your Extraordinary Favor

Using similar logic as the aspect of spurring an AI Asymmetry on your behalf, suppose that you are already above the capabilities of the other side that is using AI. Ergo, you are not starting at an equal posture. You fortunately are already on the top side.

You might want to anyway secure an even greater advantage. Therefore, you arm yourself with AI. This takes your head and shoulders above the other side.

Inadvertent Undercutting Of AI Asymmetry To Your Disfavor

I doubt that you want to hear about this possibility. Please realize that dealing with AI is not all roses and ice cream cakes.

It could be that when you arm yourself with AI, you actually undercut yourself. If you were already less than the AI of the other side, you are now down in a deeper hole. If you were on equal terms, you are now at a disadvantage. If you were above the other side, you are now equal to or below it.

How could that happen?

You might be shocked to ponder that the AI you adopt is going to lead you astray. This easily could occur. Just because you have AI in your corner does not mean it is useful. You might be using the AI and it provides advice that you don’t necessarily think is apt, but you decide to go with it anyway. Your logic at the time was that since you went to the trouble to obtain the AI, you might as well depend upon it.

The AI you are using might be defective. Or it might be poorly devised. There is a slew of reasons why the AI might be giving you shaky advice. Those that blindly accept whatever the AI says to do are bound to find themselves in a world of hurt. I’ve covered such predicaments in my column, such as the link here.

The bottom line is that there is absolutely no guarantee that just because you arm yourself with AI you are going to win at the AI Asymmetry game.

You might arrive at a level playing field. You might gain an advantage. And, regrettably, you need to be cautious since it could be that you sink to downward levels when armed with AI.

To some degree, that is why AI Ethics and Ethical AI is such a crucial topic. The precepts of AI Ethics get us to remain vigilant. AI technologists can at times become preoccupied with technology, particularly the optimization of high-tech. They aren’t necessarily considering the larger societal ramifications. Having an AI Ethics mindset and doing so integrally to AI development and fielding is vital for producing appropriate AI.

Besides employing AI Ethics, there is a corresponding question of whether we should have laws to govern various uses of AI. New laws are being bandied around at the federal, state, and local levels that concern the range and nature of how AI should be devised. The effort to draft and enact such laws is a gradual one. AI Ethics serves as a considered stopgap, at the very least, and will almost certainly to some degree be directly incorporated into those new laws.

Be aware that some adamantly argue that we do not need new laws that cover AI and that our existing laws are sufficient. In fact, they forewarn that if we do enact some of these AI laws, we will be killing the golden goose by clamping down on advances in AI that proffer immense societal advantages. See for example my coverage at the link here and the link here.

At this juncture of this weighty discussion, I’d bet that you are desirous of some illustrative examples that might showcase this topic. There is a special and assuredly popular set of examples that are close to my heart. You see, in my capacity as an expert on AI including the ethical and legal ramifications, I am frequently asked to identify realistic examples that showcase AI Ethics dilemmas so that the somewhat theoretical nature of the topic can be more readily grasped. One of the most evocative areas that vividly presents this ethical AI quandary is the advent of AI-based true self-driving cars. This will serve as a handy use case or exemplar for ample discussion on the topic.

Here’s then a noteworthy question that is worth contemplating: Does the advent of AI-based true self-driving cars illuminate anything about AI Asymmetry, and if so, what does this showcase?

Allow me a moment to unpack the question.

First, note that there isn’t a human driver involved in a true self-driving car. Keep in mind that true self-driving cars are driven via an AI driving system. There isn’t a need for a human driver at the wheel, nor is there a provision for a human to drive the vehicle. For my extensive and ongoing coverage of Autonomous Vehicles (AVs) and especially self-driving cars, see the link here.

I’d like to further clarify what is meant when I refer to true self-driving cars.

Understanding The Levels Of Self-Driving Cars

As a clarification, true self-driving cars are ones where the AI drives the car entirely on its own and there isn’t any human assistance during the driving task.

These driverless vehicles are considered Level 4 and Level 5 (see my explanation at this link here), while a car that requires a human driver to co-share the driving effort is usually considered at Level 2 or Level 3. The cars that co-share the driving task are described as being semi-autonomous, and typically contain a variety of automated add-ons that are referred to as ADAS (Advanced Driver-Assistance Systems).

There is not yet a true self-driving car at Level 5, and we don’t yet even know if this will be possible to achieve, nor how long it will take to get there.

Meanwhile, the Level 4 efforts are gradually trying to get some traction by undergoing very narrow and selective public roadway trials, though there is controversy over whether this testing should be allowed per se (we are all life-or-death guinea pigs in an experiment taking place on our highways and byways, some contend, see my coverage at this link here).

Since semi-autonomous cars require a human driver, the adoption of those types of cars won’t be markedly different than driving conventional vehicles, so there’s not much new per se to cover about them on this topic (though, as you’ll see in a moment, the points next made are generally applicable).

For semi-autonomous cars, it is important that the public needs to be forewarned about a disturbing aspect that’s been arising lately, namely that despite those human drivers that keep posting videos of themselves falling asleep at the wheel of a Level 2 or Level 3 car, we all need to avoid being misled into believing that the driver can take away their attention from the driving task while driving a semi-autonomous car.

You are the responsible party for the driving actions of the vehicle, regardless of how much automation might be tossed into a Level 2 or Level 3.

Self-Driving Cars And AI Asymmetry

For Level 4 and Level 5 true self-driving vehicles, there won’t be a human driver involved in the driving task.

All occupants will be passengers.

The AI is doing the driving.

One aspect to immediately discuss entails the fact that the AI involved in today’s AI driving systems is not sentient. In other words, the AI is altogether a collective of computer-based programming and algorithms, and most assuredly not able to reason in the same manner that humans can.

Why is this added emphasis about the AI not being sentient?

Because I want to underscore that when discussing the role of the AI driving system, I am not ascribing human qualities to the AI. Please be aware that there is an ongoing and dangerous tendency these days to anthropomorphize AI. In essence, people are assigning human-like sentience to today’s AI, despite the undeniable and inarguable fact that no such AI exists as yet.

With that clarification, you can envision that the AI driving system won’t natively somehow “know” about the facets of driving. Driving and all that it entails will need to be programmed as part of the hardware and software of the self-driving car.

Let’s dive into the myriad of aspects that come to play on this topic.

First, it is important to realize that not all AI self-driving cars are the same. Each automaker and self-driving tech firm is taking its approach to devising self-driving cars. As such, it is difficult to make sweeping statements about what AI driving systems will do or not do.

Furthermore, whenever stating that an AI driving system doesn’t do some particular thing, this can, later on, be overtaken by developers that in fact program the computer to do that very thing. Step by step, AI driving systems are being gradually improved and extended. An existing limitation today might no longer exist in a future iteration or version of the system.

I hope that provides a sufficient litany of caveats to underlie what I am about to relate.

Let’s sketch out a scenario that showcases AI Asymmetry.

Contemplate the seemingly inconsequential matter of where self-driving cars will be roaming to pick up passengers. This seems like an abundantly innocuous topic.

At first, assume that AI self-driving cars will be roaming throughout entire towns. Anybody that wants to request a ride in a self-driving car has essentially an equal chance of hailing one. Gradually, the AI begins to primarily keep the self-driving cars roaming in just one section of town. This section is a greater money-maker and the AI has been programmed to try and maximize revenues as part of the usage in the community at large (this underscores the mindset underlying optimization, namely focusing on just one particular metric and neglecting other crucial factors in the process).

Community members in the impoverished parts of the town turn out to be less likely to be able to get a ride from a self-driving car. This is because the self-driving cars were further away and roaming in the higher revenue part of the town. When a request comes in from a distant part of town, any other request from a closer location would get a higher priority. Eventually, the availability of getting a self-driving car in any place other than the richer part of town is nearly impossible, exasperatingly so for those that lived in those now resource-starved areas.

Out goes the vaunted mobility-for-all dreams that self-driving cars are supposed to bring to life.

You could assert that the AI altogether landed on a form of statistical and computational bias, akin to a form of proxy discrimination (also often referred to as indirect discrimination). Realize that the AI wasn’t programmed to avoid those poorer neighborhoods. Let’s be clear about that in this instance. No, it was devised instead to merely optimize revenue, a seemingly acceptable goal, but this was done without the AI developers contemplating other potential ramifications. That optimization in turn unwittingly and inevitably led to an undesirable outcome.

Had they included AI Ethics considerations as part of their optimization mindset, they might have realized beforehand that unless they crafted the AI to cope with this kind of oversizing on one metric alone, they might have averted such dour results. For more on these types of issues that the widespread adoption of autonomous vehicles and self-driving cars are likely to incur, see my coverage at this link here, describing a Harvard-led study that I co-authored on these topics.

In any case, assume that the horse is already out of the barn and the situation is not immediately amenable to overarching solutions.

What might those that want to use those self-driving cars do?

The most apparent approach would be to work with community leaders on getting the automaker or self-driving tech firm to reconsider how they have set up the AI. Perhaps put pressure on whatever licensing or permits that have been granted for the deployment of those self-driving cars in that city or town. These are likely viable means of bringing about positive changes, though it could take a while before those efforts bear fruit.

Another angle would be to arm yourself with AI.

Envision that someone has cleverly devised an AI-based app that works on your smartphone and deals with the AI of the automaker or fleet operator that is taking in requests for rides. It could be that the AI you are using exploits key elements of the other AI such that a request for a self-driving car by you is given heightened priority. Note that I am not suggesting that anything illegal is taking place, but instead that the AI on your side has been developed based on discovered “features” or even loopholes in the other AI.


The story about brazenly fighting back against the AI of the self-driving cars fleet operator by getting armed with AI brings up additional AI Ethics controversies and considerations.

For example:

  • If one person can make use of AI to give them an advantage over an AI of some other system, how far can this go in terms of possibly crossing AI Ethics boundaries (I convince the self-driving cars to come to me and my friends, to the exclusion of all others)?
  • Also, is there any semblance of AI Ethics consideration that if someone knows about or is armed with AI to do battle with other AI, should those remaining people that do not have that balancing AI be somehow alerted to the AI and be able to arm themselves accordingly too?

In the end, all of this is taking us to a future that seems eerie, consisting of an AI arms race. Who will have the AI that they need to get around and survive and who will not? Will there always be one more AI that comes along and sparks the need for a counterbalancing AI?

Carl Sagan, the venerated scientist, provided this sage wisdom about especially cataclysmic arms races: “The nuclear arms race is like two sworn enemies standing waist deep in gasoline, one with three matches, the other with five.”

We must decisively aim to keep our feet dry and our heads clear when it comes to an ever-looming AI arms race.


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