Prioritizing Policies for Furthering Responsible Artificial Intelligence in the United States

TL;DR

This paper reviews and ranks nine policies for promoting responsible AI in the U.S., emphasizing strategic prioritization across institutions to maximize impact and address barriers.

Contribution

It introduces a framework for ranking AI policies by impact and feasibility, providing tailored recommendations for different U.S. institutions.

Findings

Pre-deployment audits and assessments have high impact but face adoption barriers.

Post-deployment accountability is highly impactful, especially for legislation.

Prioritized policies vary by institution type, requiring strategic implementation.

Abstract

Several policy options exist, or have been proposed, to further responsible artificial intelligence (AI) development and deployment. Institutions, including U.S. government agencies, states, professional societies, and private and public sector businesses, are well positioned to implement these policies. However, given limited resources, not all policies can or should be equally prioritized. We define and review nine suggested policies for furthering responsible AI, rank each policy on potential use and impact, and recommend prioritization relative to each institution type. We find that pre-deployment audits and assessments and post-deployment accountability are likely to have the highest impact but also the highest barriers to adoption. We recommend that U.S. government agencies and companies highly prioritize development of pre-deployment audits and assessments, while the U.S. national legislature should highly prioritize post-deployment accountability. We suggest that U.S. government agencies and professional societies should highly prioritize policies that support responsible AI research and that states should highly prioritize support of responsible AI education. We propose that companies can highly prioritize involving community stakeholders in development efforts and supporting diversity in AI development. We advise lower levels of prioritization across institutions for AI ethics statements and databases of AI technologies or incidents. We recognize that no one policy will lead to responsible AI and instead advocate for strategic policy implementation across institutions.