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 ascribe shared responsibility to both Human and AI systems, rather than either separately. When the outcome was negative, participants were more likely to attribute responsibility to a single entity, but not consistently towards the human or the AI. These results build on the understanding of how individuals cast blame and praise for shared interactions involving AI systems.

How do individual differences in personal morality affect perceptions and judgments of morally contentious behaviours from AI systems? By applying Moral Foundations Theory (MFT) to the context of AI, this study sought to develop a predictive Bayesian model for assessing moral judgements based on individual differences in moral constitution. Participants (N=240) were asked to assess six different scenarios, carefully designed to elicit reflection on the behaviour of AI systems. Together, with results from the Moral Foundations Questionnaire, we performed both Bayesian modelling and reflexive thematic analysis to investigate the associations between individual differences in moral foundations and judgements of the AI systems. Results revealed a mild association between individual MFT scores and judgments of AI behaviours. Qualitative responses suggested a participant’s technical understanding of AI systems, rather than intrinsic moral values, predominantly influenced their judgments, with those who judged the behaviour as wrong tending to attribute a greater degree of agency to the AI systems.