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

arXiv: https://arxiv.org/abs/2108.05379 · HTML: https://mlsanity.com/paper/2108.05379/

## Key facts

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

## Built on by (most cited first, 16 of 16)

- [Explainable Artificial Intelligence (XAI) 2.0: A Manifesto of Open Challenges and Interdisciplinary Research Directions](https://mlsanity.com/paper/2310.19775/) (2023)
- [From Shapley Values to Generalized Additive Models and back](https://mlsanity.com/paper/2209.04012/) (2022)
- [Post-Hoc Explanations Fail to Achieve their Purpose in Adversarial Contexts](https://mlsanity.com/paper/2201.10295/) (2022)
- ["There Is Not Enough Information": On the Effects of Explanations on Perceptions of Informational Fairness and Trustworthiness in Automated Decision-Making](https://mlsanity.com/paper/2205.05758/) (2022)
- [Interpretable Machine Learning in Physics: A Review](https://mlsanity.com/paper/2503.23616/) (2025)
- [Towards More Trustworthy and Interpretable LLMs for Code through Syntax-Grounded Explanations](https://mlsanity.com/paper/2407.08983/) (2024)
- [A Means-End Account of Explainable Artificial Intelligence](https://mlsanity.com/paper/2208.04638/) (2022)
- [ExpProof : Operationalizing Explanations for Confidential Models with ZKPs](https://mlsanity.com/paper/2502.03773/) (2025)
- [Requirements for Explainability and Acceptance of Artificial Intelligence in Collaborative Work](https://mlsanity.com/paper/2306.15394/) (2023)
- [Software Doping Analysis for Human Oversight](https://mlsanity.com/paper/2308.06186/) (2023)
- [SHAPCA: Consistent and Interpretable Explanations for Machine Learning Models on Spectroscopy Data](https://mlsanity.com/paper/2603.19141/) (2026)
- [Automated Trustworthiness Testing for Machine Learning Classifiers](https://mlsanity.com/paper/2406.05251/) (2024)
- [Automated Trustworthiness Oracle Generation for Machine Learning Text Classifiers](https://mlsanity.com/paper/2410.22663/) (2024)
- [Mapping the Trust Terrain: LLMs in Software Engineering -- Insights and Perspectives](https://mlsanity.com/paper/2503.13793/) (2025)
- [Healthy Distrust in AI systems](https://mlsanity.com/paper/2505.09747/) (2025)
- [What Did I Learn? Operational Competence Assessment for AI-Based Trajectory Planners](https://mlsanity.com/paper/2510.00619/) (2025)

## 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.