- The paper critiques strict adherence to "evidence-based" AI policy, arguing high evidentiary standards can delay necessary regulation by drawing parallels to tactics used by tobacco and fossil fuel industries.
- The authors identify biases in current AI evidence, including selective corporate disclosure, disparities in measuring impacts, focus on precedented risks, and lack of representation in research.
- They propose fifteen process-oriented regulatory objectives, such as establishing AI governance institutes and requiring model registration and risk assessments, to facilitate generating necessary evidence for informed policy.
A Critical Analysis of "Pitfalls of Evidence-Based AI Policy"
The paper, "Pitfalls of Evidence-Based AI Policy," authored by Stephen Casper, David Krueger, and Dylan Hadfield-Menell, offers a comprehensive critique of the concept of "evidence-based AI policy," which is increasingly advocated for in regulatory circles. The authors argue that while evidence is indispensable for policy formulation, requiring an excessively high evidentiary standard can inhibit timely regulatory actions, potentially compromising the mitigation of certain AI-associated risks. The paper navigates the intricate landscape of AI policy-making by challenging the mantra of evidence-based approaches and discussing how such frameworks have historically served to delay action and shield industry interests.
Key Arguments and Evidence
The authors caution against the perils of demanding substantial evidence before enacting AI regulations. They draw historical parallels to the tobacco and fossil fuel industries, where stringent evidentiary requirements delayed necessary regulatory interventions, producing tangible societal harms. The paper illustrates how the rhetoric of evidence-based policy has often been utilized as a strategic tool by industries to eschew immediate regulatory actions under the guise of insufficient evidence.
In their analysis, the authors highlight several biases in the existing body of AI evidence. These include selective disclosure by tech companies, a disparity between easy-to-measure and hard-to-measure impacts, a focus on precedented rather than unprecedented impacts, and a lack of representation in the AI research community. Such biases inherently challenge the neutrality of the scientific process, thereby skewing evidence and potentially neglecting critical risks and outgroup concerns.
Regulatory Recommendations
Crucially, the paper delineates a set of fifteen regulatory objectives aimed at facilitating the production and utilization of evidence in AI policy-making. These goals emphasize process regulation over substantive regulation and include the establishment of AI governance institutes, model registration, comprehensive risk assessments, and improved documentation and transparency measures.
The authors argue that these process-oriented regulations can enable a more informed governance framework by generating the necessary evidence base to prudently manage AI risks. They assert that a scarcity of evidence should prompt the enactment of such policies, not serve as a pretext for regulatory inertia.
Implications and Future Directions
By proposing concrete process regulations, the paper addresses the pragmatic aspects of AI governance, steering the policy discourse away from speculative risks and towards actionable insights. The authors posit that embracing such regulation can mitigate the danger of relying on the prevailing, potentially biased, evidentiary landscape dominated by industry-led research.
The paper's nuanced take on AI policy serves as a vital contribution to ongoing debates about the governance of AI technologies. It encourages policymakers to consider the latent biases within the current evidence base and advocate for regulatory frameworks that proactively facilitate the accumulation of impartial, comprehensive, and actionable evidence.
Conclusion
"Pitfalls of Evidence-Based AI Policy" advances a critical perspective on AI governance, challenging entrenched notions of evidence-based policy by highlighting its potential shortcomings. By advocating for process-based regulations that prioritize generating robust evidence, the authors outline a pragmatic pathway for AI regulation that can adapt to the complexities and uncertainties inherent in emerging technologies. The paper posits that such an approach will not only enhance our understanding of AI risks but also empower policymakers and society to engage in meaningful debates on AI's societal impacts.