Designing for Accountable Agents: a Viewpoint
Stephen Cranefield, Nir Oren

TL;DR
This paper explores the concept of accountability within multi-agent systems, providing a survey, a practical example, and outlining research challenges to enable autonomous agents to participate in accountability processes.
Contribution
It offers a comprehensive survey of accountability across disciplines, presents a realistic MAS application example, and identifies key research challenges with initial solutions for accountable autonomous agents.
Findings
Survey of accountability in multiple disciplines
Example of MAS application demonstrating accountability benefits
Initial research challenges and solutions for accountable agents
Abstract
AI systems are becoming increasingly complex, ubiquitous and autonomous, leading to increasing concerns about their impacts on individuals and society. In response, researchers have begun investigating how to ensure that the methods underlying AI decision-making are transparent and their decisions are explainable to people and conformant to human values and ethical principles. As part of this research thrust, the need for accountability within AI systems has been noted, but this notion has proven elusive to define; we aim to address this issue in the current paper. Unlike much recent work, we do not address accountability within the human organisational processes of developing and deploying AI; rather we consider what it would it mean for the agents within a multi-agent system (MAS), potentially including human agents, to be accountable to other agents or to have others accountable to…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
