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

TL;DR

This paper investigates how cognitive biases, especially biased error attribution, affect human decision-making in multi-agent AI systems with delayed feedback, revealing systematic misattributions and their implications.

Contribution

It introduces the concept of attribution bias in multi-agent human-AI interactions with delayed outcomes and provides experimental evidence of its effects on decision adjustments.

Findings

Participants show asymmetric responses to gains and losses.

Participants often misattribute responsibility across AI agents.

Delayed feedback amplifies cognitive biases in multi-agent settings.

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 phenomenon as a form of attribution bias, manifested as biased error attribution under delayed feedback. Our findings highlight how cognitive biases can be amplified in human-AI systems with delayed outcomes and multiple autonomous agents, underscoring the need for decision-support systems that better support causal understanding and learning over time.