# Is Trust Correlated With Explainability in AI? A Meta-Analysis

arXiv: https://arxiv.org/abs/2504.12529 · HTML: https://mlsanity.com/paper/2504.12529/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 49 references that are in the map.
- First posted on arXiv 16 Apr 2025.

## Builds on (most cited first, 12 of 12)

- [Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Prompting](https://mlsanity.com/paper/2305.04388/) (2023)
- [A Survey Of Methods For Explaining Black Box Models](https://mlsanity.com/paper/1802.01933/) (2018)
- [Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges](https://mlsanity.com/paper/2103.11251/) (2021)
- [Effect of Confidence and Explanation on Accuracy and Trust Calibration in AI-Assisted Decision Making](https://mlsanity.com/paper/2001.02114/) (2020)
- [Explainable Artificial Intelligence (XAI) 2.0: A Manifesto of Open Challenges and Interdisciplinary Research Directions](https://mlsanity.com/paper/2310.19775/) (2023)
- [AI in Finance: Challenges, Techniques and Opportunities](https://mlsanity.com/paper/2107.09051/) (2021)
- ["Help Me Help the AI": Understanding How Explainability Can Support Human-AI Interaction](https://mlsanity.com/paper/2210.03735/) (2022)
- [Dear XAI Community, We Need to Talk! Fundamental Misconceptions in Current XAI Research](https://mlsanity.com/paper/2306.04292/) (2023)
- [Combat COVID-19 Infodemic Using Explainable Natural Language Processing Models](https://mlsanity.com/paper/2103.00747/) (2021)
- [AI Ethics Principles in Practice: Perspectives of Designers and Developers](https://mlsanity.com/paper/2112.07467/) (2021)
- [Know Your Model (KYM): Increasing Trust in AI and Machine Learning](https://mlsanity.com/paper/2106.11036/) (2021)
- [AI perspectives in Smart Cities and Communities to enable road vehicle automation and smart traffic control](https://mlsanity.com/paper/2104.03150/) (2021)

## Built on by (most cited first, 8 of 8)

- [Accessibility Scout: Personalized Accessibility Scans of Built Environments](https://mlsanity.com/paper/2507.23190/) (2025)
- [Rule-Based Moral Principles for Explaining Uncertainty in Natural Language Generation](https://mlsanity.com/paper/2509.07190/) (2025)
- [ScenarioBench: Trace-Grounded Compliance Evaluation for Text-to-SQL and RAG](https://mlsanity.com/paper/2509.24212/) (2025)
- [From keywords to semantics: Perceptions of large language models in data discovery](https://mlsanity.com/paper/2510.01473/) (2025)
- [Kantian-Utilitarian XAI: Meta-Explained](https://mlsanity.com/paper/2510.03892/) (2025)
- [Context-aware, Ante-hoc Explanations of Driving Behaviour](https://mlsanity.com/paper/2511.14428/) (2025)
- [Exploring Trust Calibration in XAI - The Impact of Exposing Model Limitations to Lay Users](https://mlsanity.com/paper/2605.18036/) (2026)
- [Asterism: Exploring and Synthesizing Scattered Observations into Literature-Grounded Hypotheses and Theories](https://mlsanity.com/paper/2610.02673/) (2026)

## Abstract (arXiv)

This study critically examines the commonly held assumption that explicability in artificial intelligence (AI) systems inherently boosts user trust. Utilizing a meta-analytical approach, we conducted a comprehensive examination of the existing literature to explore the relationship between AI explainability and trust. Our analysis, incorporating data from 90 studies, reveals a statistically significant but moderate positive correlation between the explainability of AI systems and the trust they engender among users. This indicates that while explainability contributes to building trust, it is not the sole or predominant factor in this equation. In addition to academic contributions to the field of Explainable AI (XAI), this research highlights its broader socio-technical implications, particularly in promoting accountability and fostering user trust in critical domains such as healthcare and justice. By addressing challenges like algorithmic bias and ethical transparency, the study underscores the need for equitable and sustainable AI adoption. Rather than focusing solely on immediate trust, we emphasize the normative importance of fostering authentic and enduring trustworthiness in AI systems.