When Humans and Machines Fail: Understanding Responsibility in Human–AI Collaboration

Abstract

The present study aims to address this gap by examining how individuals attribute responsibility in situations involving human–AI collaboration. Using scenario-based methods, participants are presented with decision-making situations in which a negative outcome occurs and are asked to evaluate the extent to which responsibility lies with the human actor, the AI system, or both. The central research question guiding this study is: How do individuals attribute responsibility between human actors and artificial intelligence systems when a decision failure occurs during human–AI collaboration? In addition, the study explores whether perceptions of AI as more autonomous or agent-like influence responsibility judgments, as well as whether existing expectations about human accountability shape how blame is assigned. By examining these patterns, the study seeks to provide insight into how people understand responsibility in increasingly common forms of human–technology interaction