Responsibility Attribution in Human Interactions with Everyday AI Systems

Abstract

How do individuals perceive AI systems as responsible entities in everyday collaborations between humans and AI? Drawing on psychological literature from attribution theory, praise-blame asymmetries and negativity bias, this study investigated the effects of perspective (actor vs observer) and outcome favorability (positive vs negative) on how participants (N=321) attributed responsibility for outcomes resulting from shared human-AI decision-making. Both Bayesian modelling and reflexive thematic analysis of results revealed that, overall, participants were more likely to attribute greater responsibility to the AI systems. When the outcome was positive, participants were more likely to ascrib..