mlsanityData to 14 Oct 2026

On the Relation of Trust and Explainability: Why to Engineer for Trustworthiness

arXiv 2108.05379 ↗First posted 11 Aug 2021cs.SE cs.CYcited by 16

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Abstract (arXiv)

Recently, requirements for the explainability of software systems have gained prominence. One of the primary motivators for such requirements is that explainability is expected to facilitate stakeholders' trust in a system. Although this seems intuitively appealing, recent psychological studies indicate that explanations do not necessarily facilitate trust. Thus, explainability requirements might not be suitable for promoting trust. One way to accommodate this finding is, we suggest, to focus on trustworthiness instead of trust. While these two may come apart, we ideally want both: a trustworthy system and the stakeholder's trust. In this paper, we argue that even though trustworthiness does not automatically lead to trust, there are several reasons to engineer primarily for trustworthiness -- and that a system's explainability can crucially contribute to its trustworthiness.

Key facts. Cited by 16 papers in the map (data through 14 Oct 2026). First posted on arXiv 11 Aug 2021.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 4; 2023: 3; 2024: 3; 2025: 5; 2026: 1.

16 papers.

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  1. 2023Explainable Artificial Intelligence (XAI) 2.0: A Manifesto of Open Challenges and Interdisciplinary Research Directionscited by 125
  2. 2022From Shapley Values to Generalized Additive Models and backcited by 60
  3. 2022Post-Hoc Explanations Fail to Achieve their Purpose in Adversarial Contextscited by 53
  4. 2022"There Is Not Enough Information": On the Effects of Explanations on Perceptions of Informational Fairness and Trustworthiness in Automated Decision-Makingcited by 19
  5. 2025Interpretable Machine Learning in Physics: A Reviewcited by 14
  6. 2024Towards More Trustworthy and Interpretable LLMs for Code through Syntax-Grounded Explanationscited by 11
  7. 2022A Means-End Account of Explainable Artificial Intelligencecited by 5
  8. 2025ExpProof : Operationalizing Explanations for Confidential Models with ZKPscited by 2
  9. 2023Requirements for Explainability and Acceptance of Artificial Intelligence in Collaborative Workcited by 1
  10. 2023Software Doping Analysis for Human Oversightcited by 1
  11. 2026SHAPCA: Consistent and Interpretable Explanations for Machine Learning Models on Spectroscopy Datacited by 1
  12. 2024Automated Trustworthiness Testing for Machine Learning Classifierscited by 0
4 more
  1. 2024Automated Trustworthiness Oracle Generation for Machine Learning Text Classifierscited by 0
  2. 2025Mapping the Trust Terrain: LLMs in Software Engineering -- Insights and Perspectivescited by 0
  3. 2025Healthy Distrust in AI systemscited by 0
  4. 2025What Did I Learn? Operational Competence Assessment for AI-Based Trajectory Plannerscited by 0