Three Laws Blog: Law and Artificial Intelligence

Lee on Synthetic Data and the Future of AI @UCDavisLaw

Peter Lee, University of California, Davis, School of Law, is publishing Synthetic Data and the Future of AI in volume 110 of the Cornell Law Review. Here is the abstract.

The future of artificial intelligence (AI) is synthetic. Several of the most prominent technical and legal challenges of AI derive from the need to amass huge amounts of real-world data to train machine learning (ML) models. Collecting such real-world data can be highly difficult and can threaten privacy, introduce bias in automated decision making, and infringe copyrights on a massive scale. This Article explores the emergence of a seemingly paradoxical technical creation that can mitigate—though not completely eliminate—these concerns: synthetic data. Increasingly, data scientists are using simulated driving environments, fabricated medical records, fake images, and other forms of synthetic data to train ML models. Artificial data, in other words, is being used to train artificial intelligence. Synthetic data offers a host of technical and legal benefits; it promises to radically decrease the cost of obtaining data, sidestep privacy issues, reduce automated discrimination, and avoid copyright infringement. Alongside such promise, however, synthetic data offers perils as well. Deficiencies in the development and deployment of synthetic data can exacerbate the dangers of AI and cause significant social harm. In light of the enormous value and importance of synthetic data, this Article sketches the contours of an innovation ecosystem to promote its robust and responsible development. It identifies three objectives that should guide legal and policy measures shaping the creation of synthetic data: provisioning, disclosure, and democratization. Ideally, such an ecosystem should incentivize the generation of high-quality synthetic data, encourage disclosure of both synthetic data and processes for generating it, and promote multiple sources of innovation. This Article then examines a suite of “innovation mechanisms” that can advance these objectives, ranging from open source production to proprietary approaches based on patents, trade secrets, and copyrights. Throughout, it suggests policy and doctrinal reforms to enhance innovation, transparency, and democratic access to synthetic data. Just as AI will have enormous legal implications, law and policy can play a central role in shaping the future of AI.

Download the article from SSRN at the link.

Diamantis on Reasonable AI: A Negligence Standard @ProfDiamantis @IowaLawSchool @VandLRev

Mihailis Diamantis, University of Iowa College of Law, is publishing Reasonable AI: A Negligence Standard in volume 77 of the Vanderbilt Law Review. Here is the abstract.

Even as artificial intelligence promises to turbocharge social and economic progress, its human costs are becoming apparent. By design, AI behaves in unexpected ways. That is how it finds unanticipated solutions to complex problems. But unpredictability also means that AI will sometimes harm us. To curtail these harms, scholars and lawmakers have proposed strict regulations for firms developing safe algorithms and strict corporate liability for injuries that nonetheless occur. These rigid “solutions” go too far. They dampen innovation and disadvantage domestic firms in the international technology race. The law needs a more nuanced framework that balances progress with fairness. Tort law offers a compelling template, but the challenge is to adapt its distinctly human notion of fault to algorithms. Tort law’s central liability standard is negligence, which compares the defendant's behavior to other "reasonable" people's behavior. But there is no clear comparison class for AI. Assessing algorithms by reference to people would set too low of a bar—AI can and should outperform reasonable humans on many tasks. Assessing AI instead by reference to itself is often impossible—there are not enough algorithms in many contexts to establish a meaningful baseline. This Paper offers a novel negligence standard for AI. Rather than compare any given AI to humans or to other algorithms, the law should compare it to both. By this hybrid measure, an algorithm would be deemed negligent if it causes injury more frequently than the combined incident rate for all actors—both human and AI—engaged in the same type of conduct. This negligence standard has three attractive features. First, it offers a baseline even when there are very few comparable algorithms. Second, it incentivizes firms to release all and only algorithms that make us safer overall. Third, the standard evolves over time, demanding more of AI as algorithms improve.

Download the article from SSRN at the link.

Ying Hu on Unjust Enrichment Law and AI @nus_law

Ying Hu, National University of Singapore, Faculty of Law, is publishing Unjust Enrichment Law and AI in The Cambridge Handbook of Private Law and Artificial Intelligence, Chapter 13 (forthcoming). Here is the abstract.

Unjust enrichment is a plausible cause of action for individuals whose data has been collected and used without their consent to train, develop, or improve AI systems. Disgorgement of profits may be possible in some situations where the defendant has unlawfully collected or used personal data. Gain-based remedies have a number of advantages in this context, including the fact that it may be relatively easy to ascertain the gain, but demonstrating the loss will be considerably harder. However, contractual pre-emption may limit the utility of claims for unjust enrichment.

Download the essay from SSRN at the link.