Fairness and Explanation in AI-Informed Decision Making

AI-assisted decision-making that impacts individuals raises critical questions about transparency and fairness in artificial intelligence (AI). Much research has highlighted the reciprocal relationships between transparency/explanation decision-making. Thus, considering their impact on user trust or perceived simultaneously benefits responsible use of socio-technical AI systems, but currently receives little attention. In this paper, we investigate effects explanations human-AI fairness, respectively, specific AI-based scenarios. A study simulating two health insurance medical treatment scenarios provided important insights. Due to global pandemic restrictions thereof, studies were conducted as online surveys. From participant’s perspective, was found affect only under condition a low level, with level reducing trust. However, adding helped users increase perspective our work levels introduced decreased users’ perceptions while high increased fairness. The addition definitely perception Furthermore, application influenced results show statements applications is complex: need consider not type degree introduced, also which used.