AI, Data Value Augmentation, Latent Data Rights, and Post Hoc Regulation

There is a correlation between the power of AI capabilities and the data on which it is employed. It translates as follows: The greater the power/capability of the AI app, the more likely the latent data value tends to increase and with that the higher probability that stringent laws will emerge and be applied to the use of those applications. This is an augmentation phenomena and the challenges it creates are already made clear in the battle between the ACLU and Clearview AI over the latter’s facial recognition capabilities and the ends to which it is put to use.

Data, publicly available, and cast into the internet ecosystem with little, if any careful thought by its owners, suddenly becomes endowed with a significantly elevated status and import once sufficiently powerful AI is applied to it. This capability signals, and the Clearview AI case highlights it, that we are paving the way into a legal framework that accommodates a recognition that data, even freely/carelessly given data, contains latent rights. Rights that will be subject to post hoc regulation because powerful AI apps, serving as a catalyzing agent, generated value that would (absent AI) have no, or very little value.

Consumer consent (one of the key issues in the ACLU v. Clearview AI litigation) is something that has accumulated a bad vibe, if you will; it has become a joke. Overall, it is mistakenly awarded by courts and regulators with overly high importance when evaluating whether certain company actions can be considered “fair” (think of that in the context of an FTC perspective). Now, consumers have a poor track record (to say the least) of reading and understanding the terms and conditions that companies present to them.¹ This has led courts and regulators to take, generally speaking, a paternalistic view, finding that no valid consent was provided and invalidating the use sought by the company that collected the data.

Absent appropriate caution, courts and regulators asked to deal with powerful AI applications use of publicly available data may be tempted to automatically ascribe a higher value to it and impose post hoc restrictions. The problem with this is that meaningful consumer consent is unlikely going to get easier to obtain. AI applications, on the other hand, are only going to be more and more capable as time goes by. A default finding that latent data rights exist in data that was essentially carelessly dumped out of the car’s window is an ambitious and misguided post hoc regulatory approach.

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1. There are many reasons for why this is so and I have proposed methods and structures that use AI-powered computational law applications that can help empower consumers to make better choices. See the Maximizing Representative Efficacy posts, starting with this one.