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

2026-04-26

AI Summary

This study addresses the limitations of existing research, which predominantly relies on trust metrics to assess human reliance on AI and fails to capture “appropriate reliance”—the extent to which users judiciously adopt AI advice in suitable contexts. Through a systematic literature review and construct analysis, this work is the first to integrate and distinguish three measurement perspectives of appropriate reliance: traditional, appropriateness-based, and dominance-oriented, thereby clarifying its theoretical boundaries relative to trust and mere dependence. The analysis reveals a fragmented landscape of current measurement approaches and proposes a unified, objective evaluation framework. This framework establishes a reproducible and comparable empirical foundation for future research on human–AI collaborative systems.

Technology Category

• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Humans and AI: Learning Human Values and Preferences
• Application Domains: Humanities & Computational Social Science

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

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.

Problem

• appropriate reliance
• human-AI decision-making
• trust
• measurement constructs
• objective metrics

Innovation

• appropriate reliance
• human-AI decision-making
• objective metrics
• trust measurement
• AI advice