AI Ethics And The Law Are Dabbling With AI Disgorgement Or All-Out Destruction Of AI As A Remedy For AI Wrongdoing, Possibly Even For Misbehaving…

Will we seek to use AI disgorgement or the destroying of AI when some AI wrongdoing is alleged, and ... [+] if so, can we pull it off?

You might say that society seems nearly obsessed with indestructibility.

We relish movies and sci-fi stories that showcase superhumans that are seemingly indestructible. Those of us that are commonplace non-superhuman people dream about magically becoming indestructible. Companies market products claiming that their vaunted goods are supposedly indestructible.

The famous comedian Milton Berle used to tell a pretty funny joke about items that are allegedly indestructible: I bought my son an indestructible toy. Yesterday he left it in the driveway. It broke my car. Thats an uproarious side splitter for those that endlessly are seeking to discover anything that could be somehow contended as being indestructible.

I bring up this rather fascinating topic to cover a matter that is rising quickly as an important consideration when it comes to the advent of Artificial Intelligence (AI). I will pose the contentious bubbling topic as a simple question that perhaps surprisingly has a quite complex answer.

In brief, is AI entirely susceptible to destruction or could there be AI that ostensibly could be asserted as being indestructible or thereabouts?

This is a vital aspect underlying recent efforts dealing with both the legal and ethical ramifications of AI. Legally, as you will see in a moment, doors are opening toward using the destruction of an AI system as a means of providing a legal remedy as to a consequence of some pertinent unlawful or unethical wrong. Note that the field of AI Ethics also is weighing in on the considered use of destruction of AI or the comparable deletion of AI. 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.

Mull this whole conundrum over for a moment.

Should we do be seeking to delete or destroy AI?

And, can we do it, even if we wanted to do so?

Ill go ahead and unpack the controversial topic and showcase some examples to highlight the tradeoffs involved in this mind-bending quandary.

First, lets get some language on the table to ensure we are singing the same tune. The suitably lofty way to phrase the topic consists of indicating that we are aiming to undertake AI Disgorgement. Some also use interchangeably the notion of Algorithmic Disgorgement. For sake of discussion herein, I am going to equate the two catchphrases. Technically, you can persuasively argue that they are not one and the same. I think the discussion here can suffice by modestly blurring the difference.

That being said, you might not be readily familiar at all with the word disgorgement since it usually arises in a legal-related context. Most law dictionaries depict disgorgement as the act of giving up something due to a legal demand or compulsion.

A noted article in the Yale Journal of Law & Technology entitled Algorithms and Economic Justice: A Taxonomy of Harms and a Path Forward for the Federal Trade Commission by Rebecca Slaughter, a Commissioner of the Federal Trade Commission (FTC), described the matter this way: One innovative remedy that the FTC has recently deployed is algorithmic disgorgement. The premise is simple: when companies collect data illegally, they should not be able to profit from either the data or any algorithm developed using it (August 2021).

In that same article, the point is further made by highlighting some prior akin instances: This novel approach was most recently deployed in the FTCs case against Everalbum in January 2021. There, the Commission alleged that the company violated its promises to consumers about the circumstances under which it would deploy facial-recognition software. As part of the settlement, the Commission required the company to delete not only the ill-gotten data but also any facial recognition models or algorithms developed with users photos or videos. The authority to seek this type of remedy comes from the Commissions power to order relief reasonably tailored to the violation of the law. This innovative enforcement approach should send a clear message to companies engaging in illicit data collection in order to train AI models: Not worth it.

Just recently, additional uses of the disgorgement method have come to the fore. Consider this reporting in March of this year: The Federal Trade Commission has struggled over the years to find ways to combat deceptive digital data practices using its limited set of enforcement options. Now, its landed on one that could have a big impact on tech companies: algorithmic destruction. And as the agency gets more aggressive on tech by slowly introducing this new type of penalty, applying it in a settlement for the third time in three years could be the charm. In a March 4 settlement order, the agency demanded that WW International formerly known as Weight Watchers destroy the algorithms or AI models it built using personal information collected through its Kurbo healthy eating app from kids as young as 8 without parental permission (in an article by Kate Kaye, March 14, 2022, Protocol online blog).

Lest you think that this disgorgement idea is solely a U.S. viewpoint, various assessments of the draft European Union (EU) Artificial Intelligence Act suggest that the legal language therein can be interpreted as allowing for a withdrawal of an AI system (i.e., some would say this assuredly amounts to the AI being subject to destruction, deletion, or disgorgement). See my coverage at the link here.

Much of this talk about deleting or destroying an AI system is usually centered on a particular type of AI known as Machine Learning (ML) or Deep Learning (DL). ML/DL is not the only way to craft AI. Nonetheless, the increasing availability of ML/DL and its use has created quite a stir for being both beneficial and yet also at times abysmal.

ML/DL is merely 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.

AI and especially the widespread advent of ML/DL has gotten societal dander up about the ethical underpinnings of how AI might be sourly devised. You might be aware that when this 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 Ive 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.

How does this tend to arise in the case of using Machine Learning?

Well, straightforwardly, if humans have historically been making patterned decisions incorporating untoward biases, the odds are that the data used to train ML/DL reflects this in subtle but significant ways. Machine Learning or Deep Learning computational pattern matching will blindly 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 (GIGO). 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.

This is also why the tenets of AI Ethics have been emerging as an essential cornerstone for those that are crafting, fielding, or using AI. We ought to expect AI makers to embrace AI Ethics and seek to produce Ethical AI. Likewise, society should be on the watch that any AI unleashed or promogulated into use is abiding by AI Ethics precepts.

To help illustrate the AI Ethics precepts, consider the set as stated by the Vatican in the Rome Call For AI Ethics and that Ive covered in-depth at the link here. This articulates six primary AI ethics principles:

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

Ive 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:

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.

In a moment, I will be coming back to the AI Disgorgement topic and will be pointing out that we need to separate the destruction or deletion of AI into two distinct categories: (1) sentient AI, and (2) non-sentient AI. Lets set some foundational ground on those two categories so well be ready to engage further in the AI Disgorgement matter.

Please be abundantly aware that there isnt any AI today that is sentient.

We dont have sentient AI. We dont 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). To those of you that are seriously immersed in the AI field, none of this foregoing pronouncement is surprising or raises any eyebrows. Meanwhile, there are outsized headlines and excessive embellishment that might confound people into assuming that we either do have sentient AI or that we are on the looming cusp of having sentient AI any coming day.

Please realize that todays 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 isnt any AI today that has a semblance of common sense and nor has any of the cognitive wonderment of robust human thinking.

All told, we are today utilizing non-sentient AI and someday we might have sentient AI (but that is purely speculative). Both kinds of AI are obviously of concern for AI Ethics and we need to be aiming toward Ethical AI no matter how it is constituted.

In the case of the AI Disgorgement associated with sentient AI, we can wildly play a guessing game of nearly infinite varieties. Maybe sentient AI will cognitively be like humans and exhibit similar mental capacities. Or we could postulate that sentient AI will be superhuman and go beyond our forms of thinking. The ultimate in sentient AI would seem to be super-intelligence, something that might be so smart and cunning that we cannot today even conceive of the immense thinking prowess. Some suggest that our minds will be paltry in comparison. This super-duper AI will run rings around us in a manner comparable to how we today can outthink ants or caterpillars.

If there turns out to be AI that is sentient, we are possibly going to be willing to anoint such AI with a form of legal personhood, see my analysis at the link here. The concept is that we will provide AI with a semblance of human rights. Maybe not verbatim. Maybe a special set of rights. Who knows?

In any case, you could conjure up the seemingly provocative notion that we cannot just summarily wipe out or destroy sentient AI, even if we can technologically do so. Sentient AI might be construed as a veritable living organism in terms of cognitive capacity and innately having a right to live (depending upon the definition of being alive). There might ultimately be a stipulated legal process involved. This includes that we cannot necessarily exercise the death penalty upon a sentient AI (whoa, just wait until we as a society get embroiled in that kind of a societal debate).

I doubt that we would be willing to make the same AI Ethical posture for the non-sentient AI. Though some are trying to contend that todays non-sentient AI ought to be classified as a variant associated with legal personhood, this seems to be a steeply uphill battle. Can a piece of contemporary software that is not sentient be granted legal rights on par with humans or even animals? It sure seems like a stretch (but there are advocates fervently aiming for this, see my coverage at the link here).

Heres what this all implies.

Assuming we dont grant todays non-sentient AI as embodying the regal legal anointing of personhood, the choice of deleting or destroying such non-sentient AI would decidedly not be reasonably equated to the destruction of a living organism. The wiping out of non-sentient AI is nothing more than the same as deleting that dating app from your smartphone or erasing those excess pictures of your trip to a wonderland forest from your laptop. You can delete or destroy those bits of data and software without having a guilty conscience and without having overstepped the law in terms of having harmed a sentient living creature.

You might assume that this pronouncement summarily settles the AI Disgorgement conundrum as it relates to non-sentient AI.

Sorry, the world is never as straightforward as it might initially seem.

Get ready for a twist.

Suppose that we created a non-sentient AI that was leading us towards being able to cure cancer. The company that had developed the AI did something else that the firm should not have done and has gotten into serious legal trouble with various governmental authorities. As part of a remedy imposed upon the firm, the company is compelled to completely delete the AI, including all data and documentation associated with the AI.

The government took that company to task and assured that those wrongdoers can no longer profit from the AI that they had devised. Unfortunately, in the same breath, we have perhaps shot our own foot because the AI had capabilities that were leading us toward curing cancer. We ended up tossing out the baby with the bathwater, as it were.

The point is that we could have a variety of bona fide reasons to keep AI intact. Rather than deleting it or scrambling it, we might wish to ensure that the AI remains whole. The AI is going to be allowed to perform some of its actions in a limited manner. We want to leverage whatever AI can do for us.

A handy rule would then seem to be that the notion of AI Disgorgement should be predicated on a semblance of context and sensibility as to when this form of a remedy is suitably applicable. Sometimes it might be fully applicable, while in other instances not so. You could also try to find ways to split the apple, perhaps keeping some part of the AI that was deemed as beneficial while seeking to have destruction or deletion for the portions that are considered within the remedy deriving scope.

Of course, doing a piecemeal deletion or destruction is not a piece of cake either. It could be that the part you want to keep is integrally woven into the part you want to have destroyed. Trying to separate the two could be problematic. In the end, you might have to abandon the deletion and simply agree to allow the whole to remain, or you might have to toss in the towel and destroy the whole kit and kaboodle.

It all depends.

Time to tackle another hefty consideration.

Weve so far covered the issues underpinning the basis for wanting to bring forth an AI Disgorgement. Meanwhile, we have just now sneaked into that discussion the other next important element to consider, namely whether deleting or destroying AI is altogether always feasible.

In the preceding dialogue, we kind of assumed at face value that we can destroy or delete AI if we wanted to do so. The one twist that was mentioned involved trying to separate out the parts of an AI system that we wanted to keep intact versus the parts that we wanted to delete or destroy. That can be hard to do. Even if it is hard to accomplish, we would still be on relatively cogent turf to claim that it inevitably could be technologically attained (we might need to rebuild parts that we destroyed, putting those back into place to support the other part that we didnt want to destroy).

Slightly change the perspective and ruminate on whether we really always can in fact destroy or delete AI if we wish to do so. Put aside the AI Ethics question and focus exclusively on the technological question of destructive feasibility (I am loath to utter the words put aside the AI Ethics question since the AI Ethics question is always a vital and inseparable consideration for AI, but I hope you realize that I am using this as a figure of speech for purposes of directing attention only, thanks).

Well make this into two lines of reasoning:

I would submit that the answer to both of those questions is a qualified no (Id pretty much be on the rather safe technological ground for saying no since there is always a potential chance that we could not destroy or delete the AI, as I will elaborate on next). In essence, a lot of the time the answer would probably be yes in the case of non-sentient AI, while in the case of the sentient AI the answer is maybe, but nobody can say either way for sure due to not knowing what the sentient AI is going to be or even if it will arise.

In the case of sentient AI, there is a myriad of fanciful theories that can be postulated.

If the sentient AI is superhuman or super-intelligent, you can try to argue that the AI would outsmart us humans and not allow itself to be wiped out. Presumably, no matter what we try, this outsized AI will always be a step ahead of us. We might even try to leverage some human-friendly instance of this sentient super-duper AI to destroy another sentient AI that we are otherwise unable to delete via our own methods. Be wary though that the helpful AI later turns evildoer and we are left at the mercy of this AI that we are hence unable to get rid of.

For those of you that prefer a happy face version of the futuristic sentient AI, maybe we theorize that any sentient AI would be willing to get destroyed and want to actively do so if humans wished it so. This more understanding and sympathetic sentient AI would be able to realize when it is time to go. Rather than fighting its own destruction, it would welcome being destroyed when the time comes for such action. Perhaps the AI does the work for us and opts to self-destruct.

The conjecture about sentient AI can roam in whatever direction you dream of. There arent particularly any rules about what is possible. One supposes that the realities of physics and other natural constraints would come to bear, though maybe a super-intelligent sentient AI knows of ways to overcome everything we take for granted as reality.

Speaking of reality, lets shift our attention to the non-sentient AI of today.

You might be tempted to believe that we can always without fail opt to destroy or delete any of todays AI. Envision that a company has devised an AI system that governmental authorities order be disgorged. The firm is legally required to destroy or delete the AI system.

Easy-peasy, it seems, just press a delete button and poof, the AI system is no longer around. We do this with no longer needed apps and no longer wanted data files on our laptops and smartphones. No special computer techie skills are needed. The company can comply with the regulatory order in minutes.

We can walk through the reasons why this presumed ease of AI destruction or deletion is not as straightforward as you might initially assume.

First, a notable question surrounds the exact scope of what is meant when you say that an AI system is to be destroyed or deleted. One facet is the programming code that comprises the AI. Another facet would be any data associated with the AI.

The developers of the AI might have generated many versions of the AI while crafting the AI. Lets simplify things and say that there is a final version of the code that is the one running and has become the target for being disgorged. Okay, the company deletes that final version. The deed is done!

But, turns out that those earlier versions are all still sitting around. It might be relatively childs play to essentially resurrect the now-deleted AI by merely using one of those earlier versions. You take an earlier version, make modifications to bring it up to par, and you are back in business.

An obvious way to try and prevent this kind of deletion skirting would be to stipulate that any and all prior versions of the AI must be destroyed. This would seem to force the company into seriously finding any older versions and making sure those get deleted too.

One twist is that suppose the AI contained a significant portion of widely available open-source code. The developers had originally decided that to build the AI they would not start from scratch. Instead, they grabbed up a ton of open-source code and used it as the backbone for their AI. They do not own the open-source code. They do not control the open-source code. They only copied it into their AI creation.

Now we have a bit of a problem.

The company complies with the order to destroy their AI. They delete their copy of the code and all versions of it that they possess. They delete all of their internal documentation. Meanwhile, they are not able to get rid of the open-source that comprises (lets say) the bulk of their AI system since it is not something they legally own and have no direct control over. The firm seems to have done what it could do.

Would you say that the offending AI was in fact destroyed or deleted?

The firm would likely insist that they did so. The governing authority would seem to have a hard time contending otherwise.

They might be able to quickly resurrect the AI by just going out to grab the widely available open-source and adding the pieces by doing some programming based on their knowledge of what the added portions consisted of. They dont use any of the prior offending code that they had fully deleted. They dont use any of the documentation that they had deleted. Voila, they have a new AI system that they would argue is not the AI that they had been ordered to disgorge.

I trust that you can see how these kinds of cat and mouse games can be readily played.

There are lots more twists.

Suppose the AI that is to be disgorged was based on the use of Machine Learning. The ML could be a program that the company developed on its own, but more likely these days the ML is an algorithm or model that the firm selected from an online library or collection (there are lots and lots of these readily available).

The firm deletes the instance of the ML that they downloaded and are using. The exact same ML algorithm or model is still sitting in a publicly available online library and potentially accessible for comers that want to use it. The governmental authority might have no means to restrict or cause a disgorgement of that online library.

Thats just the start of the difficulties involved in destroying or deleting AI, including for example the use of Machine Learning. As mentioned earlier, ML and DL typically entail feeding data into the ML/DL. If the firm still has the data that they previously used, they could download another copy of the ML/DL algorithm or model from the online library and reconstitute the AI via feeding the data once again into what is essentially the same ML/DL that they had used before.

You might astutely clamor that the data the firm had been using needs to also be encompassed by the disgorgement order. Sure, lets assume that this is so.

If the data is entirely within the confines of the firm, they presumably would be able to destroy or delete the data. Problem solved, one would say. But, suppose the data was based on various external sources, all of which are outside the scope of the destruction order since they are not owned by and not controlled by the offending firm.

The crux is that you could from other external sources grab copies of the data, grab a copy of the ML/DL algorithm, and reconstitute the AI system. In some cases, this might be expensive to undertake and could require gobs of time, while in other instances it might be doable in short order. It all depends on various factors such as how much the data needs to be modified or transformed, and the same goes for the parameter setting and training of the ML/DL.

We also need to consider what the meaning of destroying or deleting consists of.

You undoubtedly know that when you delete a file or app from your computer, the chances are that the electronically stored item is not yet fully deleted. Typically, the operating system updates a setting indicating that the file or app is to be construed as having been deleted. This is a convenience if you want to bring back the file or app. The operating system can merely flip the flag to indicate that the once seemingly deleted file or app is now active again.

Even if you have the operating system perform a more determined deletion, there is a likelihood that the file or app still sits somewhere. It might be on a backup storage device. It might be archived. If you are using a cloud-based online service, copies are likely residing there too. Not only would you need to find all of those shadow copies, but you would also need to perform various specialized cybersecurity erasure actions to try and ensure that the bits and bytes of those files and apps are completely written over and in a sense truly deleted or destroyed.

Note that I just mentioned the notion of a shadow.

We have at least three types of shadows to be thinking about when it comes to AI disgorgement:

1) Shadow copies of the AI

2) Shadow algorithms associated with the AI

3) Shadow data associated with the AI

Imagine that an order for an AI disgorgement instructs a company to proceed with destroying or deleting the data associated with the AI, but the firm can keep around the algorithm (perhaps allowing this if the algorithm is seemingly nothing more than one that you can find in any online ML library anyway).

Turns out that the algorithm itself essentially can be said to have its own kind of data, such as particular settings that underpin the algorithm. The effort to train the ML will usually entail having the ML figure out what parameter settings need to be calibrated. If you are only ordered to get rid of the training dataset per se, those other data-related parameter settings are likely still going to remain. This suggests that the AI can be somewhat readily reconstituted, or you could even argue that the AI wasnt deleted at all and you simply got rid of the earlier used training data that perhaps you no longer care about anyway. There is also a high chance that a form of imprint remains from the training data, which Ive discussed at the link here.

Getting rid of the training data might also be challenging if the data comes from a variety of third-party sources. Sure, you might be able to force the company to delete their in-house instance of the compiled data, but if the data exists at those other sources beyond their scope, the same data could be likely reassembled. This might be costly or might be inexpensive to do, depending upon the circumstances.

Throughout this discussion, we have focused on the notion of having a particular company be the target for undertaking an AI disgorgement. This might be satisfying and serve as an appropriate remedy associated with that company. On the other hand, this is not necessarily going to somehow eradicate or destroy the AI as it might exist or be reconstituted beyond the scope of the targeted company.

The AI might be copied to zillions of other online sites that the company has no means to access and cannot force a deletion to take place. The AI might be rebuilt from scratch by others that are aware of how the AI works. You could even have former employees of the firm that leave the company and opt to reuse their AI development skills to construct the essentially same AI elsewhere, which would be argued by them as based on their knowledge and skills, thus not being an infringing or subjected copy of the AI disgorgement order.

A perhaps apt analogy to the AI disgorgement troubles might be the advent of computer viruses.

The chances of hunting down and deleting all copies of a viral computer virus are generally slim, especially due to the legal questions of where the virus might be residing (such as across international borders) and the technological trickery of the computer virus trying to hide (Ive discussed the emergence of AI-based polymorphic computer viruses that are electronic self-adapting shape-shifters).

Furthermore, compounding the challenges, there is always the presumed capability of constructing the same or roughly equivalent computer virus by those that are well-versed in the design and crafting of computer viruses all told.

The rest is here:

AI Ethics And The Law Are Dabbling With AI Disgorgement Or All-Out Destruction Of AI As A Remedy For AI Wrongdoing, Possibly Even For Misbehaving...