arXiv:2606.10906v1 Announce Type: cross Abstract: We study models for human-AI teaming through the lens of statistical calibration. We assume the team consists of an AI model and human -- both of which are calibrated with respect to some partitioning of the feature space -- and expose how the calibration assumptions propagate into the teaming framework. In particular, we consider frameworks that either (i) combine human and model predictions or (ii) delegate prediction responsibility to either a human or model. We show via theoretical and empirical results that existing methods for combination
The increasing deployment of autonomous AI systems across critical domains makes understanding human-AI interaction and trust paramount.
Improving human-AI teaming is crucial for the effective and safe integration of AI into complex decision-making processes, impacting productivity, safety, and operational efficiency.
This research provides a more rigorous, empirically grounded framework for designing and evaluating human-AI collaboration, moving beyond purely anecdotal or intuitive approaches.
- · AI developers
- · High-stakes industries (e.g., healthcare, defense)
- · Human-AI interface designers
- · AI systems with poor calibration
- · Organizations relying on unoptimized human-AI workflows
More robust and trustworthy AI systems will be deployed in critical human-in-the-loop applications.
Increased trust and efficiency in human-AI partnerships could dramatically accelerate process automation and decision support capabilities.
Delegation frameworks could become sophisticated enough for AI to dynamically adjust its autonomy based on real-time human cognitive load and system uncertainty, redefining 'control'.
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Read at arXiv cs.LG