From Accuracy to Readiness: Metrics and Benchmarks for Human-AI Decision-Making
Artificial intelligence (AI) systems are deployed as collaborators in human decision-making. Yet, evaluation practices focus primarily on model accuracy rather than whether human-AI teams are prepared to collaborate safely and effectively. Empirical evidence shows that many failures arise from miscalibrated reliance, including overuse when AI is wrong and underuse when it is helpful. This paper proposes a measurement framework for evaluating human-AI decision-making centered on team readiness. We introduce a four part taxonomy of evaluation metrics spanning outcomes, reliance behavior, safety signals, and learning over time, and connect these metrics to the Understand-Control-Improve (U-C...
| Authors | Min Hun Lee |
|---|---|
| Primary category | cs.HC |
| Published | 19 March 2026 |
| Last updated at source | 19 March 2026 |
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