Low-rank human-like agents are trusted more and blamed less in human-autonomy teaming

If humans are to team with artificial teammates, factors that influence trust and shared accountability must be considered when designing agents. This study investigates the influence of anthropomorphism, rank, decision cost, and task difficulty on trust in human-autonomous teams (HAT) and how blame is apportioned if shared tasks fail. Participants (N = 31) completed repeated trials with an artificial teammate using a low-fidelity variation of an air-traffic control game. We manipulated anthropomorphism (human-like or machine-like), military rank of artificial teammates using three-star (superiors), two-star (peers), or one-star (subordinate) agents, the perceived payload of vehicles with people or supplies onboard, and task difficulty with easy or hard missions using a within-subject design. A behavioural measure of trust was inferred when participants accepted agent recommendations, and a measure of no trust when recommendations were rejected or ignored. We analysed the data for trust using binomial logistic regression. After each trial, blame was apportioned using a 2-item scale and analysed using a one-way repeated measures ANOVA. A post-experiment questionnaire obtained participants' power distance orientation using a seven-item scale. Possible power-related effects on trust and blame apportioning are discussed. Our findings suggest that artificial agents with higher levels of anthropomorphism and lower levels of rank increased trust and shared accountability, with human team members accepting more blame for team failures.

When company decisions harm or help the environment: the influence of social context and affective state on moral and causal responsibility attributions

Holding companies accountable for their decisions’ environmental side effects becomes increasingly important in the light of current debates on the climate crisis. The present study investigated a young sample’s (n = 925) causal and moral responsibility attributions to a company’s manager and their subordinate employee, who were either young or middle-aged. The agents jointly made a profit-oriented decision which either harmed or helped the environment. Results were analyzed with respect to the valence of the side effect, the agents’ social role, the agents’ age group, and participants’ affective state. We successfully replicated findings from prior studies, showing that more intention and moral responsibility is ascribed to the manager in case of a negative side effect than in case of a positive side effect, and that the manager deserves more blame for negative side effects than the employee, whereas the employee deserves more praise for positive side effects. Additionally, responsibility attributions varied with the agents’ age group, participants’ positive and negative affective state, and participants’ gender. By incorporating these hitherto neglected factors, the study complements existing theories of responsibility attribution and intergroup perception. The findings further highlight the contribution of affective mechanisms as driving factors of responsibility attributions. Combining moral responsibility research, intergroup research, and affective measures promises to foster the understanding of how and to whom people ascribe blame, praise, and causal responsibility for environmental consequences.