Keeping an Eye on AI: A Framework for Effective Human Oversight of AI Systems

TL;DR

This paper proposes a comprehensive framework for designing, implementing, and evaluating effective human oversight mechanisms in AI systems, addressing current gaps in understanding and practice.

Contribution

It introduces a foundational framework, a documentation template, and highlights open challenges for human oversight of AI, integrating insights from multiple disciplines.

Findings

A formal definition and architecture for human oversight of AI.

A template for documenting oversight architectures and processes.

Identification of key open research challenges.

Abstract

The use of Artificial Intelligence (AI) in high-risk, decision-making scenarios presents technical, safety, and normative challenges; problems that may only be ameliorated by human oversight. However, notions of human oversight lack a common foundational understanding: oversight architectures are not well defined, the roles involved remain unclear, and implementation steps are opaque. Hence, researchers and practitioners struggle to determine how to design, implement, and evaluate systems that enable effective human oversight. This paper advances a practical framework for effective human oversight of AI systems, based on a cross-disciplinary perspective that draws on insights from computer science, human-computer interaction, psychology, philosophy, and law. The core contributions are: (1) a foundational framework, with a working definition, architecture and processes for effective human oversight of AI systems; (2) an initial template for documenting oversight architectures and processes, applied to diverse domains; and (3) a synthesis of open research challenges that need to be considered in the emerging field of effective human oversight of AI systems.