Pitfalls of Evidence-Based AI Policy

TL;DR

This paper critically examines the challenges of implementing evidence-based AI policy, highlighting risks of over-reliance on evidence standards and proposing regulatory goals to improve risk identification and policy effectiveness.

Contribution

It introduces a set of 15 regulatory goals aimed at enhancing evidence-seeking in AI policy and analyzes opportunities for various countries to adopt these strategies.

Findings

High evidentiary standards can neglect certain AI risks.

Historical policy debates show evidence-based rhetoric can delay action.

Several countries have opportunities to improve evidence-seeking policies.

Abstract

Nations across the world are working to govern AI. However, from a technical perspective, there is uncertainty and disagreement on the best way to do this. Meanwhile, recent debates over AI regulation have led to calls for "evidence-based AI policy" which emphasize holding regulatory action to a high evidentiary standard. Evidence is of irreplaceable value to policymaking. However, holding regulatory action to too high an evidentiary standard can lead to systematic neglect of certain risks. In historical policy debates (e.g., over tobacco ca. 1965 and fossil fuels ca. 1985) "evidence-based policy" rhetoric is also a well-precedented strategy to downplay the urgency of action, delay regulation, and protect industry interests. Here, we argue that if the goal is evidence-based AI policy, the first regulatory objective must be to actively facilitate the process of identifying, studying, and deliberating about AI risks. We discuss a set of 15 regulatory goals to facilitate this and show that Brazil, Canada, China, the EU, South Korea, the UK, and the USA all have substantial opportunities to adopt further evidence-seeking policies.