Is Trust Correlated With Explainability in AI? A Meta-Analysis
It addresses the assumption that explainability inherently boosts trust in AI, with implications for accountability and trust in critical domains like healthcare and justice, though it is incremental in synthesizing existing literature.
This meta-analysis examined the relationship between AI explainability and user trust, finding a statistically significant but moderate positive correlation across 90 studies, indicating explainability contributes to but is not the sole factor in building trust.
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.