In today's society, where Artificial Intelligence (AI) has gained a vital role, concerns regarding user's trust have garnered significant attention. The use of AI systems in high-risk domains have often led users to either under-trust it, potentially causing inadequate reliance or over-trust it, resulting in over-compliance. Therefore, users must maintain an appropriate level of trust. Past research has indicated that explanations provided by AI systems can enhance user understanding of when to trust or not trust the system. However, the utility of presentation of different explanations forms still remains to be explored especially in high-risk domains. Therefore, this study explores the impact of different explanation types (text, visual, and hybrid) and user expertise (retired police officers and lay users) on establishing appropriate trust in AI-based predictive policing. While we observed that the hybrid form of explanations increased the subjective trust in AI for expert users, it did not led to better decision-making. Furthermore, no form of explanations helped build appropriate trust. The findings of our study emphasize the importance of re-evaluating the use of explanations to build [appropriate] trust in AI based systems especially when the system's use is questionable. Finally, we synthesize potential challenges and policy recommendations based on our results to design for appropriate trust in high-risk based AI-based systems.

本研究应对高风险领域中用户对人工智能系统信任不足的问题，探讨不同类型解释（文本、视觉和混合）对用户信任的影响。研究发现，虽然混合形式的解释增加了专家用户的主观信任，但未能改善决策质量，也未能建立适当的信任。因此，本文强调需要重新评估在可疑系统中使用解释的有效性，并提出政策建议，以促进高风险领域人工智能系统的适当信任。

即使解释也无法帮助信任[这个]根本偏见的系统：预测警务案例研究