Friday, November 17, 2023
Mihailis Diamantis (University of Iowa - College of Law) has posted Reasonable AI: A Negligence Standard on SSRN. 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.