On the Relation of Trust and Explainability: Why to Engineer for Trustworthiness
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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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Citing papers per year: 2022: 4; 2023: 3; 2024: 3; 2025: 5; 2026: 1.
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- 2023Explainable Artificial Intelligence (XAI) 2.0: A Manifesto of Open Challenges and Interdisciplinary Research Directionscited by 125
- 2022From Shapley Values to Generalized Additive Models and backcited by 60
- 2022Post-Hoc Explanations Fail to Achieve their Purpose in Adversarial Contextscited by 53
- 2022"There Is Not Enough Information": On the Effects of Explanations on Perceptions of Informational Fairness and Trustworthiness in Automated Decision-Makingcited by 19
- 2025Interpretable Machine Learning in Physics: A Reviewcited by 14
- 2024Towards More Trustworthy and Interpretable LLMs for Code through Syntax-Grounded Explanationscited by 11
- 2022A Means-End Account of Explainable Artificial Intelligencecited by 5
- 2025ExpProof : Operationalizing Explanations for Confidential Models with ZKPscited by 2
- 2023Requirements for Explainability and Acceptance of Artificial Intelligence in Collaborative Workcited by 1
- 2023Software Doping Analysis for Human Oversightcited by 1
- 2026SHAPCA: Consistent and Interpretable Explanations for Machine Learning Models on Spectroscopy Datacited by 1
- 2024Automated Trustworthiness Testing for Machine Learning Classifierscited by 0
4 more
- 2024Automated Trustworthiness Oracle Generation for Machine Learning Text Classifierscited by 0
- 2025Mapping the Trust Terrain: LLMs in Software Engineering -- Insights and Perspectivescited by 0
- 2025Healthy Distrust in AI systemscited by 0
- 2025What Did I Learn? Operational Competence Assessment for AI-Based Trajectory Plannerscited by 0
- 2026SHAPCA: Consistent and Interpretable Explanations for Machine Learning Models on Spectroscopy Data19 Mar 2026
- 2025What Did I Learn? Operational Competence Assessment for AI-Based Trajectory Planners1 Oct 2025
- 2025Healthy Distrust in AI systems14 May 2025
- 2025Interpretable Machine Learning in Physics: A Review30 Mar 2025
- 2025Mapping the Trust Terrain: LLMs in Software Engineering -- Insights and Perspectives18 Mar 2025
- 2025ExpProof : Operationalizing Explanations for Confidential Models with ZKPs6 Feb 2025
- 2024Automated Trustworthiness Oracle Generation for Machine Learning Text Classifiers30 Oct 2024
- 2024Towards More Trustworthy and Interpretable LLMs for Code through Syntax-Grounded Explanations12 Jul 2024
- 2024Automated Trustworthiness Testing for Machine Learning Classifiers7 Jun 2024
- 2023Explainable Artificial Intelligence (XAI) 2.0: A Manifesto of Open Challenges and Interdisciplinary Research Directions30 Oct 2023
- 2023Software Doping Analysis for Human Oversight11 Aug 2023
- 2023Requirements for Explainability and Acceptance of Artificial Intelligence in Collaborative Work27 Jun 2023
4 more
- 2022From Shapley Values to Generalized Additive Models and back8 Sep 2022
- 2022A Means-End Account of Explainable Artificial Intelligence9 Aug 2022
- 2022"There Is Not Enough Information": On the Effects of Explanations on Perceptions of Informational Fairness and Trustworthiness in Automated Decision-Making11 May 2022
- 2022Post-Hoc Explanations Fail to Achieve their Purpose in Adversarial Contexts25 Jan 2022
- 2023Explainable Artificial Intelligence (XAI) 2.0: A Manifesto of Open Challenges and Interdisciplinary Research Directionscited by 125
- 2022From Shapley Values to Generalized Additive Models and backcited by 60
- 2022Post-Hoc Explanations Fail to Achieve their Purpose in Adversarial Contextscited by 53
- 2022"There Is Not Enough Information": On the Effects of Explanations on Perceptions of Informational Fairness and Trustworthiness in Automated Decision-Makingcited by 19
- 2025Interpretable Machine Learning in Physics: A Reviewcited by 14
- 2024Towards More Trustworthy and Interpretable LLMs for Code through Syntax-Grounded Explanationscited by 11
- 2022A Means-End Account of Explainable Artificial Intelligencecited by 5
- 2025ExpProof : Operationalizing Explanations for Confidential Models with ZKPscited by 2
- 2023Requirements for Explainability and Acceptance of Artificial Intelligence in Collaborative Workcited by 1
- 2023Software Doping Analysis for Human Oversightcited by 1
- 2026SHAPCA: Consistent and Interpretable Explanations for Machine Learning Models on Spectroscopy Datacited by 1
- 2024Automated Trustworthiness Testing for Machine Learning Classifierscited by 0
12 most cited of 16 All 16 (JSON)
- 2026SHAPCA: Consistent and Interpretable Explanations for Machine Learning Models on Spectroscopy Data19 Mar 2026
- 2025What Did I Learn? Operational Competence Assessment for AI-Based Trajectory Planners1 Oct 2025
- 2025Healthy Distrust in AI systems14 May 2025
- 2025Interpretable Machine Learning in Physics: A Review30 Mar 2025
- 2025Mapping the Trust Terrain: LLMs in Software Engineering -- Insights and Perspectives18 Mar 2025
- 2025ExpProof : Operationalizing Explanations for Confidential Models with ZKPs6 Feb 2025
- 2024Automated Trustworthiness Oracle Generation for Machine Learning Text Classifiers30 Oct 2024
- 2024Towards More Trustworthy and Interpretable LLMs for Code through Syntax-Grounded Explanations12 Jul 2024
- 2024Automated Trustworthiness Testing for Machine Learning Classifiers7 Jun 2024
- 2023Explainable Artificial Intelligence (XAI) 2.0: A Manifesto of Open Challenges and Interdisciplinary Research Directions30 Oct 2023
- 2023Software Doping Analysis for Human Oversight11 Aug 2023
- 2023Requirements for Explainability and Acceptance of Artificial Intelligence in Collaborative Work27 Jun 2023
12 newest of 16 All 16 (JSON)