Boston University Law Review
Margot E. Kaminski, Regulating the Risks of AI, 103 BU L. Rev. 1347 (2023), available at https://scholar.law.colorado.edu/faculty-articles/1621.
Companies and governments now use Artificial Intelligence (“AI”) in a wide range of settings. But using AI leads to well-known risks that arguably present challenges for a traditional liability model. It is thus unsurprising that lawmakers in both the United States and the European Union (“EU”) have turned to the tools of risk regulation in governing AI systems.
This Article describes the growing convergence around risk regulation in AI governance. It then addresses the question: what does it mean to use risk regulation to govern AI systems? The primary contribution of this Article is to offer an analytic framework for understanding the use of risk regulation as AI governance. It aims to surface the shortcomings of risk regulation as a legal approach, and to enable readers to identify which type of risk regulation is at play in a given law. The theoretical contribution of this Article is to encourage researchers to think about what is gained and what is lost by choosing a particular legal tool for constructing the meaning of AI systems in the law.
Whatever the value of using risk regulation, constructing AI harms as risks is a choice with consequences. Risk regulation comes with its own policy baggage: a set of tools and troubles that have emerged in other fields. Risk regulation tends to try to fix problems with the technology so it may be used, rather than contemplating that it might sometimes not be appropriate to use it at all. Risk regulation works best on quantifiable problems and struggles with hard-toquantify harms. It can cloak what are really policy decisions as technical decisions. Risk regulation typically is not structured to make injured people whole. And the version of risk regulation typically deployed to govern AI systems lacks the feedback loops of tort liability. Thus the choice to use risk regulation in the first place channels the law towards a particular approach to AI governance that makes implicit tradeoffs and carries predictable shortcomings.
The second, more granular observation this Article makes is that not all risk regulation is the same. That is, once regulators choose to deploy risk regulation, there are still significant variations in what type of risk regulation they might use. Risk regulation is a legal transplant with multiple possible origins. This Article identifies at least four models for AI risk regulation that meaningfully diverge in how they address accountability.
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