Responsible Artificial Intelligence (RAI) in U.S. Federal Government : Principles, Policies, and Practices
Atul Rawal, Katie Johnson, Curtis Mitchell, Michael Walton, Diamond Nwankwo
TL;DR
The paper addresses the challenge of governing AI in the U.S. federal government by framing Responsible AI (RAI) as a lifecycle, governance-oriented approach and surveying the regulatory landscape. It outlines five RAI pillars and documents how federal policies—including EO 13859, EO 13960, EO 14110, the OMB M-24-10 memo, and the AI Bill of Rights—shape governance, risk, and transparency requirements, complemented by NIST RMF and GAO accountability frameworks. The Census Bureau case studies demonstrate operationalization of RAI through tools like model cards, a centralized AI registry, and an RAI assessment toolkit, illustrating concrete pathways for agency-specific implementation. The paper argues for bottom-up, domain-specific governance to address unique data sensitivity and use contexts, while calling for codified law to ensure long-term durability of AI governance in the face of rapid model advances and licensing challenges.
Abstract
Artificial intelligence (AI) and machine learning (ML) have made tremendous advancements in the past decades. From simple recommendation systems to more complex tumor identification systems, AI/ML systems have been utilized in a plethora of applications. This rapid growth of AI/ML and its proliferation in numerous private and public sector applications, while successful, has also opened new challenges and obstacles for regulators. With almost little to no human involvement required for some of the new decision-making AI/ML systems, there is now a pressing need to ensure the responsible use of these systems. Particularly in federal government use-cases, the use of AI technologies must be carefully governed by appropriate transparency and accountability mechanisms. This has given rise to new interdisciplinary fields of AI research such as \textit{Responsible AI (RAI)}. In this position paper we provide a brief overview of development in RAI and discuss some of the motivating principles commonly explored in the field. An overview of the current regulatory landscape relating to AI is also discussed with analysis of different Executive Orders, policies and frameworks. We then present examples of how federal agencies are aiming for the responsible use of AI, specifically we present use-case examples of different projects and research from the Census Bureau on implementing the responsible use of AI. We also provide a brief overview for a Responsible AI Assessment Toolkit currently under-development aimed at helping federal agencies operationalize RAI principles. Finally, a robust discussion on how different policies/regulations map to RAI principles, along with challenges and opportunities for regulation/governance of responsible AI within the federal government is presented.