AI Ethics And The Generational Transition From Digital Natives To AI Natives Growing Up Amidst Pervasive AI, Including Ubiquitous Self-Driving Cars

AI Ethics And The Generational Transition From Digital Natives To AI Natives Growing Up Amidst Pervasive AI, Including Ubiquitous Self-Driving Cars

You’ve undoubtedly heard of the catchphrase known as digital natives.

Most everyone has.

I’m betting though that you’ve not heard about a relatively new catchphrase, namely referred to as AI natives. You’d better get used to this latest phrase because it is going to gradually and inexorably take hold. You see, we are dancing past the olden days of digital natives and shifting into high gear as the era of AI natives unfolds. All of this has significant impacts related to AI Ethics and the advent of Ethical AI, which is a topic that my column has and continues to extensively cover, such as the link here and the link here, just to name a few.

Before we take a close look at AI natives and what the phrasing entails, we should make sure that digital natives are suitably placed on the table, as it were.

What exactly is a so-called digital native?

The general idea is that these are people that have grown up since birth in an era of digital systems such as widespread computing, everyday mobile phones, powerful laptops, and electronic tablets, vast networking via the Internet, and altogether being immersed in digital media. They are innately or natively existent in a digital world. To them, digital is the way things are. Digital is an endemically presumed aspect and they personally cannot view themselves and the world around them in any other fashion.

They are digital natives.

Their predecessors were not equally equipped. You might liken this to growing up once airplanes became a commonly accepted form of flight. Those that were around prior to the advent of being able to directly walk onto an aircraft for an airborne trip were inevitably awestruck with the reality of being able to fly. Each time they later on in life managed to take a flight they were somewhat gobsmacked. What an amazing feat to participate in. The experience of going on a plane seemed magical and nearly unimaginable.

Digital natives are ordinarily ho-hum about digital modes of communication. Sure, they sometimes are pleasantly surprised or excited when they find an added nuance of what digital can do, but on the whole, they take these matters in customary stride. Being able to leverage digital capabilities is something that they are totally comfortable with and fully expect to be undertaken when feasible to do so.

You might not realize that the catchphrase is said to have originated in an article that appeared in 2001 that described the current state of students growing up around the latest in high-tech. Per that article, the author said this about the topic: “Today’s students – K through college – represent the first generations to grow up with this new technology. They have spent their entire lives surrounded by and using computers, video games, digital music players, video cams, cell phones, and all the other toys and tools of the digital age” (Marc Prensky, “Digital Natives, Digital Immigrants,” On The Horizon).

The author postulates ways in which this generation can be distinctly labeled. After mulling over several possibilities, the paper then says this: “But the most useful designation I have found for them is Digital Natives. Our students today are all ‘native speakers’ of the digital language of computers, video games and the Internet” (per Prensky article as cited above).

You might at first think that being anointed as a digital native is perhaps a clever form of titling or titular designation but that it doesn’t really make a demonstrative difference in day-to-day living. According to the originating paper, there is a crucial difference: “It is now clear that as a result of this ubiquitous environment and the sheer volume of their interaction with it, today’s students think and process information fundamentally differently from their predecessors. These differences go far further and deeper than most educators suspect or realize” (per Prensky’s article as cited).

The gist is that being a digital native presumably does have great import. Those that are digital natives are apparently able to think and process the world around them in a more substantive way, specifically in the use and assessment of information. They are said to have an edge over those that were not of the digital native era. A digital native intrinsically uses digital means and modes, including having adjusted their thinking processes correspondingly. We are to contrastingly envision that those before the digital natives and yet amongst a digital world find themselves somewhat at a loss of how to cope and are not able to conjure a comparable mindset as those digital natives do.

As an aside, not everyone agrees that digital natives are somehow rejiggered in terms of their mental processes about the world. The notion seems pleasant enough that we might find human thinking processes as being differently calibrated as a result of growing up amidst digital tech. Some researchers argue that someone that is decidedly not a digital native can potentially equally mind adjusting to, doing so without necessarily having grown up entirely in a digital era. Acrimonious debate on this ensues.

Whether a digital native is an axiomatic and bona fide digital wizard is also an open question. In other words, the assumption often suggests that by being a digital native there is an absolutely assured correspondence that the person will be adept at and highly proficient in the use of digital technologies. This would seem a bit of a bridge too far on this labeling. I’m sure that we’ve all encountered digital natives that were not up to snuff on digital ways. Proclaiming that someone is a digital native does not guarantee their digital savviness (plus, we should keep in mind that not everywhere in the world perchance is as readily abundant in digital access and digital resources).

Let’s keep in mind those caveats as I shift into the shall we say an allied topic encompassing AI natives.

First, a quick summary about digital natives:

  • Digital natives are those of a generation raised during a digital era
  • They are said to innately embrace and be comfortable with digital technology
  • Claims are that their mindset is adjusted seamlessly to a digital world
  • Their actions and efforts are to some extent shaped by their digital versatility
  • Being digitally oriented is woven into their day-to-day existence

I trust that we can all accept those as keystone tenets for the moment.

What is an AI native?

The general idea is that people who have grown up from birth during the era of Artificial Intelligence such as widespread uses of AI on their smartphones and across the web are altogether immersed in AI and innately are existent in an AI-based world. To them, AI is the way things are. Knowing about and being around AI is a natively presumed aspect and they personally cannot view themselves and the world around them in any other fashion.

As a side note, you might have noticed that I conveniently reworded my opening paragraph defining digital natives to alter it for accommodating the AI native definitional rendition. This makes abundant sense. We are sliding from the era of digital natives into an AI natives era, for which much of the insights about digital natives can be readily recalibrated for consideration of the AI natives.

I propose herein that we take as cornerstones these five tenets about AI natives:

1) AI natives are those of a generation raised during an AI era

2) They are said to innately embrace and be comfortable with AI systems

3) Claims are that their mindset is adjusted seamlessly to an AI-based digital world

4) Their actions and efforts are to some extent shaped by their AI versatility

5) Being AI-oriented is woven into their day-to-day existence

You might recognize those tenets as once again being borrowed from the set devised about digital natives. Yes, that would seem entirely appropriate. We can examine each of these and generally anticipate that there are likely applicable to AI natives, akin to how they applied to digital natives.

One other quick point. You do not need to give up being a digital native to be an AI native. There is nothing about those two types that causes one to preclude the other. In short, you can be a digital native and also be an AI native. The odds are that you would almost certainly have to be a digital native to also be an AI native, part and parcel of the definitional lapse of time that incurs.

We should add these helpful corollaries to this discussion:

  • Being a digital native is entirely compatible with being an AI native
  • By and large, AI natives are almost certainly digital natives
  • There are digital natives that aren’t AI natives
  • We can’t say for sure whether AI natives already are in existence

That last item in the bulleted list is quite an attention-getter.

There is controversy over whether or not we are already in an AI native era or maybe we have yet to get there. The children born in recent years are at times alluded to as being AI native due to the seeming widespread use of AI. We’ve got Siri and Alexa as supposed indicators that we are now indeed in an AI era and that kids are growing up fully accustomed to AI around them.

You would though find a lot of arguments about drawing such a line in the sand. Some fervently state that we are not at all in an AI era as yet. We need to have a lot more AI before we can contentedly declare that AI has arrived. On top of that protestation, there are some that would argue that we can trace AI back to its beginnings in say the 1950s and 1960s, in which case the generations from those years are also able to be labeled as AI natives.

Makes your head spin.

It would seem reasonable perhaps to say that we will not count AI natives as starting back to the earliest days of computing. I dare say, most would hopefully agree that we need to be looking at more modern dates. The likelier starting time might be the most current generation or maybe the upcoming generation or two. We might not be able to paint a starting line until a decade from now.

Putting aside where the demarcation of being an AI native resides, we can move ahead on contemplating what the implications and ramifications of AI natives are or will be. Please go along with that pondering and set aside for sake of discussion the acrid bickering about the timing of AI natives.

What are the characteristics or capabilities that AI natives have?

I’ve got a list for you that we can briefly herein consider:

  • Has basic AI literacy as to what AI is and how AI works
  • Readily able to demystify AI
  • Not particularly susceptible to AI hype
  • Aware of AI advantages and disadvantages
  • Embraces the use of AI but with a wary and discerning eye

Before getting into some more meat and potatoes about the wild and woolly considerations underlying AI natives, let’s establish some additional fundamentals on profoundly integral 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.

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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 now return to the topic of AI natives.

Recall that I provided earlier a handy list of salient points about AI natives:

  • Has basic AI literacy as to what AI is and how AI works
  • Readily able to demystify AI
  • Not particularly susceptible to AI hype
  • Aware of AI advantages and disadvantages
  • Embraces the use of AI but with a wary and discerning eye

We can briefly examine each of the core facets that AI natives will presumably be versed in. They will have to some degree learned about AI in their schoolwork while growing up. Courses throughout the curriculum will touch upon various AI elements. To clarify, this does not mean that they will have necessarily focused directly on AI for an entire course length of attention. The idea is that since AI will crop up in all areas of scholastic endeavors, such as AI in literature, AI in science, AI in mathematics, etc., they will generally have ongoing and intermittent exposure to AI tenets.

In addition, AI natives will be surrounded by AI in one guise or another. They will interact with the likes of Alexa and Siri. They will make use of apps on their smartphones that are AI-powered. They will go to work at companies that are utilizing AI in the delivery of their goods and services. Whereas generations prior to this AI pervasiveness might be surprised or amazed at this AI use, the AI natives take the matter in stride.

We are ready now to address each of the key salient points about AI natives.

Has basic AI literacy as to what AI is and how AI works

AI natives are familiar with the basics of AI. They understand that AI consists of various computer-based capabilities. During the multi-year time frame of making use of AI, they by osmosis became aware of Natural Language Processing (NLP) and its limitations. They got used to what Machine Learning and Deep Learning consist of. They are knowledgeable about the fundamentals of AI such as computational pattern matching and computational searching techniques. They also realize that we have yet to actively be able to perform common-sense reasoning in AI to the level of human capacities, see my discussion on this at the link here.

Those are the basic AI literacy elements regarding AI techniques and technologies. This though is not the only realm of AI that AI natives will become familiar with. They will also be mindful of how AI is going to impact society. Understanding the shall we say “soft” sides of AI will be just as crucial to them as the “hard” side entailing AI technologies. This includes being cognizant of the AI Ethics principles earlier articulated herein.

Readily able to demystify AI

There are today many false claims being made about what AI can do. At times, headlines blare that AI is able to think or that we are on the verge of AI superintelligence. AI natives will not fall for this baloney. They will scoff and ridicule such wild and unsubstantiated claims.

This realization about AI allows the AI natives to demystify AI. Whether this capability will put an end to the hyperbole about AI is not clear. The odds are that there will still be attempts to shock and awe by exaggerations concerning AI in the most unashamedly outrageous terms.

Not particularly susceptible to AI hype

Akin to the ability of AI natives to demystify AI, they will be much less susceptible to AI hype. Whereas others might be drawn into false assertions about AI, the AI natives will have a wary eye.

This does not make them immune to the outsized AI claims. They are armed with sufficient comprehension of AI to sort out the wheat from the chaff when it comes to AI hysteria, but there is always the chance of nonetheless pulling the wool over even their eyes.

Aware of AI advantages and disadvantages

A particularly vital ingredient of AI natives will be their nearly innate (learned from toddler years) capacity to assess when AI is useful and when it is perhaps adversely being utilized. They will choose to use AI apps during their academic years.

Once they enter the workforce, they will be potently able to aid companies that are adopting AI. They bring sober and helpful insight into where AI can go right and where it can go wrong. This will stridently bolster the use of AI in commerce and further expand AI adoption.

Embraces the use of AI but with a wary and discerning eye

Some pundits wonder whether AI natives will be outright advocates of AI or whether they might be opponents of AI, see my coverage of AI activism at the link here. The answer is a bit more mixed. By and large, AI natives will seek to embrace and utilize AI, though doing so in a balanced and cautious way. It is hard to say whether they will categorically favor or disfavor AI.

Of course, you can certainly expect that a segment of AI natives will turn in one direction or the other. Those that are principally neutral about AI will likely be the mainstay. Meanwhile, you can assuredly anticipate that some will become outspoken advocates of AI and others will be equally strong opponents of AI.

AI Natives And The Emergence Of Autonomous Systems

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 natives, 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 Natives

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 dovetail the advent of AI natives correspondingly with the advent of autonomous vehicles and self-driving cars by pointing out the likely open-ended willingness of AI natives to make use of these new forms of autonomous transportation. By the time AI natives are a thing, the odds are that self-driving cars, self-driving trucks, self-driving motorcycles, and a plethora of other self-driving vehicles will be abundantly on our public roadways and also a thing, in that naturally combinatorial sense.

Those that came along before the AI natives are apt to stare in amazement that an autonomous vehicle has no human sitting in the driver’s seat. In contrast, the AI natives put little thought or attention toward the fact that a human is not at the wheel. This will be so customary and ordinary that it isn’t worth special focus by AI natives.

Here’s a twist that you might wish to mull over.

AI natives will eventually reach an age whereby they are having children. Those children will undoubtedly travel with the AI native “parents” via the use of self-driving cars. There is bound to be such a comfort level of using self-driving cars that these AI native parental figures will be fine with their kids using self-driving cars alone, even when an adult is not present.

I have discussed in my columns how hard a choice that would seem for those that are not AI natives. In other words, would you allow your child to travel in a self-driving car and do so without an adult in the autonomous vehicle with the child? Your first thought is likely to be that heck no, you would not let this happen. It seems crazy. For my detailed explanation of why this might be considered the new norm in an age of AI natives, see the link here.

All of this does not imply that AI natives will blindly accept the advent of self-driving cars.

AI natives will be aware of the limitations of the AI driving systems. This will cause them too cautious in other respects about self-driving cars. They will also be rightfully concerned about cybersecurity incursions of autonomous vehicles. There is also the awareness that a nation-state or some other malicious actor could attempt to take over a fleet of self-driving cars, see my coverage at the link here.

Conclusion

The generation of digital natives will gradually give way to the subsequent generations of AI natives.

If you don’t believe there is such a thing as digital natives, this would tend to suggest that you probably also take a dim view of the possibility of AI natives. That’s fine. Maybe the hullabaloo about being a digital native or an AI native is merely eye candy and nothing more.

That being said, there has been a great deal of attention and intense research devoted to analyzing and trying to make sense of digital natives, under the assumption that there is something there to be found. The same kind of analysis is indubitably going to be shifted toward eyeing AI natives.

One aspect that perhaps we can all pretty much agree on is that those that grow up amidst AI in abundance are hopefully going to have some semblance of savviness about AI. We might not label them as AI natives. We might just say that by perchance they are alive and exist during an era of AI that has gained substantially in capability and popularity.

Where will those that are fully immersed in a world of AI be opting to take humankind?

General George Patton famously stated this stark proclamation about leadership: “Lead me, follow me, or get out of my way.” We can vigorously contemplate which way those AI natives are going to go. The future will be determined by those AI natives, even if we aren’t going to refer to them by that particular moniker.

AI natives, we ask respectfully, where will you take us?

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