[2603.23419] Biased Error Attribution in Multi-Agent Human-AI Systems Under Delayed Feedback

Computer Science > Human-Computer Interaction arXiv:2603.23419 (cs) [Submitted on 24 Mar 2026] Title:Biased Error Attribution in Multi-Agent Human-AI Systems Under Delayed Feedback Authors:Teerthaa Parakh, Karen M. Feigh View a PDF of the paper titled Biased Error Attribution in Multi-Agent Human-AI Systems Under Delayed Feedback, by Teerthaa Parakh and Karen M. Feigh View PDF HTML (experimental) Abstract:Human decision-making is strongly influenced by cognitive biases, particularly under conditions of uncertainty and risk. While prior work has examined bias in single-step decisions with immediate outcomes and in human interaction with a single autonomous agent, comparatively little attention has been paid to decision-making under delayed outcomes involving multiple AI agents, where decisions at each step affect subsequent states. In this work, we study how delayed outcomes shape decision-making and responsibility attribution in a multi-agent human-AI task. Using a controlled game-based experiment, we analyze how participants adjust their behavior following positive and negative outcomes. We observe asymmetric responses to gains and losses, with stronger corrective adjustments after negative outcomes. Importantly, participants often fail to correctly identify the actions that caused failure and misattribute responsibility across AI agents, leading to systematic revisions of decisions that are weakly related to the underlying causes of poor performance. We refer to this phe...