AI Regulation Has Its Own Alignment Problem: The Technical and Institutional Feasibility of Disclosure, Registration, Licensing, and Auditing
80 Pages Posted: 30 Nov 2023
Date Written: November 15, 2023
Calls for regulating artificial intelligence (AI) are widespread, but there remains little consensus on both the specific harms that regulation can and should address and the appropriate regulatory actions to take. Computer scientists propose technical solutions that may be infeasible or illegal; lawyers propose regulation that may be technically impossible; and commentators propose policies that may backfire. AI regulation, in that sense, has its own alignment problem, where proposed interventions are often misaligned with societal values. In this Essay, we detail and assess the alignment and technical and institutional feasibility of four dominant proposals for AI regulation in the United States: disclosure, registration, licensing, and auditing. Our caution against the rush to heavily regulate AI without addressing regulatory alignment is underpinned by three arguments. First, AI regulatory proposals tend to suffer from both regulatory mismatch (i.e., vertical misalignment) and value conflict (i.e., horizontal misalignment). Clarity about a proposal’s objectives, feasibility, and impact may highlight that the proposal is mismatched with the harm intended to address. In fact, the impulse for AI regulation may in some instances be better addressed by non-AI regulatory reform. And the more concrete the proposed regulation, the more it will expose tensions and tradeoffs between different regulatory objectives and values. Proposals that purportedly address all that ails AI (safety, trustworthiness, bias, accuracy, and privacy) ignore the reality that many goals cannot be jointly satisfied. Second, the dominant AI regulatory proposals face common technical and institutional feasibility challenges—who in government should coordinate and enforce regulation, how can the scope of regulatory interventions avoid ballooning, and what standards and metrics operationalize trustworthy AI values given the lack of, and unclear path to achieve, technical consensus? Third, the federal government can, to varying degrees, reduce AI regulatory misalignment by designing interventions to account for feasibility and alignment considerations. We thus close with concrete recommendations to minimize misalignment in AI regulation.
Keywords: artificial intelligence regulation, regulation, artificial intelligence policy, technology policy, technology law
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