Responsible Artificial Intelligence (RAI) in U.S. Federal Government : Principles, Policies, and Practices

TL;DR

This paper reviews the development, principles, and regulatory landscape of Responsible AI in the U.S. federal government, highlighting agency initiatives, use cases, and tools for ensuring ethical AI deployment.

Contribution

It provides an overview of RAI principles, analyzes current policies, presents federal agency examples, and discusses tools and challenges for responsible AI governance.

Findings

Federal agencies are actively implementing RAI principles.

Existing policies are mapped to RAI principles with identified gaps.

A Responsible AI Assessment Toolkit is under development for agencies.

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.