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TRUVACE RECORD VERSION
record: TRV-2026-2788
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-11-12T17:24:07.085550Z
status: published
lens: p_space
sector: science
headline: Is Trust Correlated With Explainability in AI? A Meta-Analysis
dek: 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 us…
gain_title: (none)
problem_title: A meta-analysis of 90 studies found that explainability explains little of user trust in AI (random-effects correlation 0.194, 95% CI 0.174 to 0.210), weaker than some earlier studies had suggested.
trace_subject: (none)
gain_reading: (none)
gain_evidence: (none)
problem_reading: A meta-analysis of 90 studies found that explainability explains little of user trust in AI (random-effects correlation 0.194, 95% CI 0.174 to 0.210), weaker than some earlier studies had suggested.
problem_evidence: the correlation coefficient of 0.194 is notably weaker than what some earlier studies had suggested
quick_read: Researchers at Ontario Tech University pooled 90 studies published between 2017 and 2024 to test whether making AI systems explainable raises user trust. They found a positive, statistically significant link, but a small one: a random-effects correlation of 0.194, which falls in the range the authors class as weak (0.1 to 0.3).

The result challenges a common assumption in explainable AI research that explanations automatically build trust. The authors conclude that explainability contributes but is not the main driver, and that ethical safeguards, bias management and cultural context also matter for trust.

The studies varied a lot in design (heterogeneity I² of 78.3%), and the review focused on non-generative AI, so the figure may not carry over to chatbots and other generative models.
limitation: The review focused on non-generative AI, and the pooled studies differed substantially from one another (I² of 78.337), so the single correlation summarizes mixed designs and may not apply to generative models.
tag: Evidence-backed problem
key_points: The authors searched Scopus, IEEE and Web of Science for 2017 to 2024 and screened 1,305 records (IEEE 302, Scopus 510, Web of Science 493), keeping 90 that reported adequate statistics. | The fixed-effects model gave a correlation of 0.204 (95% CI 0.194 to 0.214); the random-effects model gave about 0.196, and the text reports 0.194 (95% CI 0.174 to 0.210). | Heterogeneity across studies was high (I² 78.337, tau² 0.009), so the authors relied on the random-effects model. | A funnel plot looked symmetrical, which the authors read as minimal publication bias. | Of the 90 studies, 60 measured trust with surveys or questionnaires and 30 used interviews or observation; 40 presented explanations through interactive dashboards and 25 through narrative explanations. | Explanation methods included model-agnostic tools such as SHAP and LIME (30 studies), intrinsically interpretable models (25) and visualization techniques (20). | The authors say designers may do better to improve a system's actual trustworthiness than to try to manipulate subjective trust.
rundown: The review followed the PRISMA protocol. Studies were excluded if they did not report main statistical indicators such as sample size, reliability or correlation coefficients, or if they used incorrect statistical methods. Qualitative and mixed studies without statistics were also dropped, leaving 90 of 1,305 records.

The included studies covered 17 on data governance and ethics, 22 on algorithmic transparency and bias mitigation, 18 on user interface design for explainability, and 33 on human-AI interaction, decision-making and trust dynamics. The AI models were mostly deep neural networks (25 studies), decision trees (20), support vector machines (15) and ensemble methods (10).

In the discussion, the authors argue that overemphasis on explainability may obscure deeper sources of distrust, and that diversity, inclusion, bias reduction and global inequalities affect trust. They recommend combining explainability with value-sensitive design and proactive risk management, and say the findings can guide policymakers and system designers on risk management and bias mitigation.
sources:
- peer_reviewed | IEEE Transactions on Technology and Society | https://doi.org/10.1109/tts.2025.3558448 | 2025-04-14
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