Across various applications, humans increasingly use black-box artificial intelligence (AI) systems without insight into these systems’ reasoning. To counter this opacity, explainable AI (XAI) methods promise enhanced transparency and interpretability. While recent studies have explored how XAI affects human–AI collaboration, few have examined the potential pitfalls caused by incorrect explanations. The implications for humans can be far-reaching but have not been explored extensively. To investigate this, we conducted a study (n = 160) on AI-assisted decision-making in which humans were supported by XAI. Our findings reveal a misinformation effect when incorrect explanations accompany correct AI advice with implications post-collaboration. This effect causes humans to infer flawed reasoning strategies, hindering task execution and demonstrating impaired procedural knowledge. Additionally, incorrect explanations compromise human–AI team performance during collaboration. With our work, we contribute to HCI by providing empirical evidence for the negative consequences of incorrect explanations on humans post-collaboration and outlining guidelines for designers of AI.

The integration of artificial intelligence (AI) into human decision-making processes at the workplace presents both opportunities and challenges. One promising approach to leverage existing complementary capabilities is allowing humans to delegate individual instances of decision tasks to AI. However, enabling humans to delegate instances effectively requires them to assess several factors. One key factor is the analysis of both their own capabilities and those of the AI in the context of the given task. In this work, we conduct a behavioral study to explore the effects of providing contextual information to support this delegation decision. Specifically, we investigate how contextual information about the AI and the task domain influence humans' delegation decisions to an AI and their impact on the human-AI team performance. Our findings reveal that access to contextual information significantly improves human-AI team performance in delegation settings. Finally, we show that the delegation behavior changes with the different types of contextual information. Overall, this research advances the understanding of computer-supported, collaborative work and provides actionable insights for designing more effective collaborative systems.

Artificial intelligence (AI) systems increasingly support decision-making across a broad range of domains. The complexity of real-world tasks, however, introduces uncertainty into the prediction capabilities of these systems. This uncertainty can manifest as aleatoric uncertainty arising from inherent variability in outcomes or epistemic uncertainty stemming from limitations in the AI system’s knowledge. While prior research has investigated uncertainty as a monolithic concept, the distinct effects of communicating aleatoric or epistemic uncertainty on humans and their reliance behavior remain unexplored. In this work, we present two behavioral experiments that systematically examine how participants rely on AI advice when faced with different types of uncertainty. While the first experiment manipulates the source of uncertainty, specifying it as either aleatoric or epistemic, the second decomposes uncertainty into its individual components, presenting aleatoric and epistemic uncertainty simultaneously. This work contributes to a deeper understanding of the multifaceted impact of different uncertainty types on human–AI interaction.