Healthy Distrust in AI systems
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Abstract (arXiv)
Under the slogan of trustworthy AI, much of contemporary AI research is focused on designing AI systems and usage practices that inspire human trust and, thus, enhance adoption of AI systems. However, a person affected by an AI system may not be convinced by AI system design alone -- neither should they, if the AI system is embedded in a social context that gives good reason to believe that it is used in tension with a person's interest. In such cases, distrust in the system may be justified and necessary to build meaningful trust in the first place. We propose the term "healthy distrust" to describe such a justified, careful stance towards certain AI usage practices. We investigate prior notions of trust and distrust in computer science, sociology, history, psychology, and philosophy, outline a remaining gap that healthy distrust might fill and conceptualize healthy distrust as a crucial part for AI usage that respects human autonomy.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 7 of its 71 references that are in the map. First posted on arXiv 14 May 2025.
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7 of 71 references.
References · 7
- 2023AI Alignment: A Comprehensive Surveycited by 263
- 2021The Values Encoded in Machine Learning Researchcited by 172
- 2021On the Relation of Trust and Explainability: Why to Engineer for Trustworthinesscited by 16
- 2024To Trust or Distrust Trust Measures: Validating Questionnaires for Trust in AIcited by 6
- 2023Forms of Understanding for XAI-Explanationscited by 4
- 2024Aligning Generalisation Between Humans and Machinescited by 2
- 2023Trust, distrust, and appropriate reliance in (X)AI: a survey of empirical evaluation of user trustcited by 1
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