From Trust to Appropriate Reliance: Measurement Constructs in Human-AI Decision-Making

TL;DR

This paper reviews how current human-AI decision-making research measures appropriate reliance, highlighting the need for standardized objective metrics to better assess human-AI interaction quality.

Contribution

It clarifies the distinction between trust and appropriate reliance, reviews existing measurement constructs, and proposes a consensus on objective metrics for future research.

Findings

Constructs for appropriate reliance are fragmented in current research.

Three perspectives on appropriate reliance: Traditional, Appropriateness, and Dominance.

Calls for standardization of objective metrics to improve comparability.

Abstract

While human-AI decision-making research has primarily used trust measurements to assess the practical usage of AI systems by their end-users, recent empirical evidence suggests that trust measurements do not inform users' appropriate reliance on AI systems. While examining the human-AI decision-making literature, in this work, we review empirical studies that assess people's appropriate reliance on AI advice, differentiating measurements and constructs of appropriate reliance from trust and mere reliance. Our analysis of literature shows that constructs for human-AI appropriate reliance are still fragmented in research. We present three views on appropriate reliance, namely Traditional, Appropriateness, and Dominance, as discussed in research. Using these views, we evaluate objective metrics reported in studies and argue for their consensus to facilitate the comparison across empirical research. We also discuss how studies employ objective metrics and examine their validity in application contexts. Our work contributes to the critical body of research on exploring objective metrics for assessing humans' appropriate reliance on AI advice.