Risk Alignment in Agentic AI Systems

TL;DR

This paper explores the importance of aligning risk attitudes in agentic AI systems to ensure safety, trust, and societal benefit, addressing normative and technical challenges in designing such systems.

Contribution

It introduces a framework for understanding and designing risk alignment in agentic AIs, highlighting key normative and technical considerations.

Findings

Risk attitudes influence AI safety and trust.

Designing calibrated risk profiles can mitigate societal risks.

Addressing responsibility gaps is crucial for accountability.

Abstract

Agentic AIs $-$ AIs that are capable and permitted to undertake complex actions with little supervision $-$ mark a new frontier in AI capabilities and raise new questions about how to safely create and align such systems with users, developers, and society. Because agents' actions are influenced by their attitudes toward risk, one key aspect of alignment concerns the risk profiles of agentic AIs. Risk alignment will matter for user satisfaction and trust, but it will also have important ramifications for society more broadly, especially as agentic AIs become more autonomous and are allowed to control key aspects of our lives. AIs with reckless attitudes toward risk (either because they are calibrated to reckless human users or are poorly designed) may pose significant threats. They might also open 'responsibility gaps' in which there is no agent who can be held accountable for harmful actions. What risk attitudes should guide an agentic AI's decision-making? How might we design AI systems that are calibrated to the risk attitudes of their users? What guardrails, if any, should be placed on the range of permissible risk attitudes? What are the ethical considerations involved when designing systems that make risky decisions on behalf of others? We present three papers that bear on key normative and technical aspects of these questions.