Given the complexity of teams involved in creating AI-based systems, how can we understand who should be held accountable when they fail? This paper reports findings about accountable AI from 26 interviews conducted with stakeholders in AI drawn from the fields of AI research, law, and policy. Participants described the challenges presented by the distributed nature of how AI systems are designed, developed, deployed, and regulated. This distribution of agency, alongside existing mechanisms of accountability, responsibility, and liability, creates barriers for (...) effective accountable design. As agency is distributed across the socio-technical landscape of an AI system, users without deep knowledge of the operation of these systems become disempowered, unable to challenge or contest when it impacts their lives. In this context, accountability becomes a matter of building systems that can be challenged, interrogated, and, most importantly, adjusted in use to accommodate counter-intuitive results and unpredictable impacts. Thus, accountable system design can work to reconfigure socio-technical landscapes to protect the users of AI and to prevent unjust apportionment of risk. (shrink)

Fostering subordinate moqi is a significant method to improve the cooperating quality and promote positive outcomes. However, little is known about the influencing factors and mechanisms of subordinate moqi. To address this issue and explore the influencing factors of subordinate moqi, we draw on self-categorization theory to develop a mediation model to examine whether and how deep-level similarity affects subordinate moqi, casting the leader-member exchange as a mediator. A two-wave online survey was conducted and 316 data was collected. A Structure (...) Equation Modeling analysis was used to test all hypotheses with Mplus 7. Results showed that the deep-level similarity could positively predict subordinate moqi and LMX, respectively. Additionally, LMX was a significant predictor of subordinate moqi as well as it mediated the positive relationships between deep-level similarity and subordinate moqi. These findings expand our understanding of the antecedents of subordinate moqi. It is suggested that developing subordinates’ deep-level similarity with supervisors and LMX relationships are instrumental in cultivating subordinate moqi which promote positive outcomes. Organizations should integrate effective management programs into managerial strategies to enhance deep-level similarity and LMX, in turn, fosters subordinate moqi. (shrink)