A Meta-Analysis on the Utility of Explainable Artificial Intelligence in Human-AI Decision-Making

Research in Artificial Intelligence (AI)-assisted decision-making is experiencing tremendous growth with a constantly rising number of studies evaluating the effect of AI with and without techniques from the field of explainable AI (XAI) on human decision-making performance. However, as tasks and experimental setups vary due to different objectives, some studies report improved user decision-making performance through XAI, while others report only negligible effects. Therefore, in this article, we present an initial synthesis of existing research on XAI studies using a statistical meta-analysis to derive implications across existing research. We observe a statistically positive impact of XAI on users' performance. Additionally, first results might indicate that human-AI decision-making yields better task performance on text data. However, we find no effect of explanations on users' performance compared to sole AI predictions. Our initial synthesis gives rise to future research to investigate the underlying causes as well as contribute to further development of algorithms that effectively benefit human decision-makers in the form of explanations.