# Prioritizing Policies for Furthering Responsible Artificial Intelligence in the United States

arXiv: https://arxiv.org/abs/2212.00740 · HTML: https://mlsanity.com/paper/2212.00740/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 79 references that are in the map.
- First posted on arXiv 30 Nov 2022.

## Builds on (most cited first, 6 of 6)

- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [The Ethics of AI Ethics -- An Evaluation of Guidelines](https://mlsanity.com/paper/1903.03425/) (2019)
- [Artificial Intelligence: the global landscape of ethics guidelines](https://mlsanity.com/paper/1906.11668/) (2019)
- [Behavioral Use Licensing for Responsible AI](https://mlsanity.com/paper/2011.03116/) (2020)
- [Principles to Practices for Responsible AI: Closing the Gap](https://mlsanity.com/paper/2006.04707/) (2020)
- [AI and Ethics -- Operationalising Responsible AI](https://mlsanity.com/paper/2105.08867/) (2021)

## Built on by (most cited first, 2 of 2)

- [Investigating Algorithm Review Boards for Organizational Responsible Artificial Intelligence Governance](https://mlsanity.com/paper/2402.01691/) (2024)
- [A short review of the main concerns in A.I. development and application within the public sector supported by NLP and TM](https://mlsanity.com/paper/2308.02042/) (2023)

## Abstract (arXiv)

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.