In recent years the use of Artificial Intelligence (AI) has become increasingly prevalent in a growing number of fields. As AI systems are being adopted in more high-stakes areas such as medicine and finance, ensuring that they are trustworthy is of increasing importance. A concern that is prominently addressed by the development and application of explainability methods, which are purported to increase trust from its users and wider society. While an increase in trust may be desirable, an analysis of literature from different research fields shows that an exclusive focus on increasing trust may not be warranted. Something which is well exemplified by the recent development in AI chatbots, which while highly coherent tend to make up facts. In this contribution, we investigate the concepts of trust, trustworthiness, and user reliance. In order to foster appropriate reliance on AI we need to prevent both disuse of these systems as well as overtrust. From our analysis of research on interpersonal trust, trust in automation, and trust in (X)AI, we identify the potential merit of the distinction between trust and distrust (in AI). We propose that alongside trust a healthy amount of distrust is of additional value for mitigating disuse and overtrust. We argue that by considering and evaluating both trust and distrust, we can ensure that users can rely appropriately on trustworthy AI, which can both be useful as well as fallible.

翻译：近年来，人工智能（AI）在越来越多的领域中得到广泛应用。随着AI系统被应用于医学、金融等高危领域，确保其可信性变得日益重要。这一关切主要通过可解释性方法的开发与应用来应对，这些方法旨在提升用户及社会对AI的信任度。尽管增强信任可能令人期待，但对不同研究领域文献的分析表明，仅聚焦于提升信任或许并不合理——近期AI聊天机器人的发展便是一个典型例证：这些系统尽管具有高度连贯性，却时常捏造事实。本文探讨了信任、可信性与用户依赖等概念。为培养对AI的合理依赖，我们需同时避免对系统的弃用与过度信任。通过对人际信任、自动化信任及（可解释）AI信任相关研究的分析，我们识别出区分（对AI的）信任与不信任的潜在价值。我们提出，适度的不信任与信任并存，对于缓解弃用与过度信任具有额外价值。我们认为，通过考量与评估信任及不信任二者，可确保用户既能合理依赖可信的AI系统——该系统兼具实用性与容错性。