🤖 AI Summary
Current AI evaluation practices overly emphasize model accuracy while neglecting calibration and safety in human-AI collaboration, often leading to misuse or underestimation of system capabilities. This work proposes a novel evaluation framework centered on “team readiness,” introducing a four-dimensional metric system that quantifies outcomes, dependency behaviors, safety signals, and learning evolution directly from real collaborative interactions. The framework integrates interaction trajectory analysis, behavioral calibration measurement, error recovery assessment, and governance indicators, aligning with the Understand–Control–Improve (U-C-I) lifecycle of human-AI teamwork. By doing so, it enables comparable and reproducible evaluations of calibration quality, error recovery capacity, and governance maturity, thereby advancing safer and more accountable research in human-AI collaboration.
📝 Abstract
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-I) lifecycle of human-AI onboarding and collaboration.
By operationalizing evaluation through interaction traces rather than model properties or self-reported trust, our framework enables deployment-relevant assessment of calibration, error recovery, and governance. We aim to support more comparable benchmarks and cumulative research on human-AI readiness, advancing safer and more accountable human-AI collaboration.