Should I stay or should I show? Learning to selectively disclose information

2026-09-30

AI Summary

This study addresses the problem that information disclosure under high costs may impair human decision-making, necessitating selective disclosure timing under budget constraints. We propose an optimal disclosure strategy based on a Value of Information (VoI) threshold rule to enhance human-AI collaboration by estimating human decision risk. Methodologically, we integrate VoI estimation, regret bound analysis, and counterfactual benchmarking to theoretically demonstrate that automated selective disclosure outperforms both full and zero disclosure, while revealing individual differences in advice compliance. Experimental results show that the proposed strategy significantly surpasses fixed baselines, effectively improving overall human-AI team performance.

Abstract

In many high-stakes settings, human decision-makers can acquire support information before making a decision. However, acquiring information is costly, and disclosure may fail to improve human decisions or may even impair them. We tackle this problem by studying selective disclosure, i.e., the problem of learning when to reveal support information to a human decision-maker under a budget constraint. We first show that the optimal policy is a threshold rule on the Value of Information (VoI), i.e., the expected reduction in human decision risk induced by disclosure. Since VoI is unknown in practice, we estimate the regime-specific human risks and bound the possible degradation of the resulting plug-in policy relative to lack of disclosure, as well as its regret relative to the optimal policy. Experiments on benchmark datasets show that selective disclosure outperforms both no disclosure and full disclosure, regardless of whether the support information is beneficial or harmful. Two user studies show that human-AI team performance can improve when disclosure is led by our learned policy and not human-selected, although this advantage varies across tasks. A counterfactual benchmark, which replaces participants' predictions with a machine-learning prediction when disclosure occurs, suggests that these differences might depend on lower adherence to advice when the information is automatically provided rather than self-requested.

Problem

• selective disclosure
• human-AI teaming
• value of information
• decision support
• budget constraint

Innovation

• Selective Disclosure
• Value of Information
• Human-AI Teaming
• Threshold Policy
• Regret Bound