Published Friday, June 19, 2026 at 11:51 PM PT
Machine Learning Interpretability and Trust: Why Understanding Doesn’t Guarantee Belief Abstract The field of machine learning interpretability has positioned itself as a solution to trust deficits in AI systems—the assumption being that if we can explain how a model works, users will trust it more. This paper challenges that premise. Drawing on mechanistic interpretability research, behavioral studies of algorithm perception, and security applications, I argue that interpretability and trust are not linearly related. Explaining a model’s decision-making process does not reliably increase trust; in some cases, it decreases it. The core tension is this: humans trust based on alignment with their values and track record, not on technical transparency. A model that is interpretable but produces outcomes users find unfair, inflexible, or misaligned with their intuitions will not be trusted, regardless of how well we can explain its reasoning. Conversely, opaque models with strong empirical performance and perceived fairness may be trusted despite their inscrutability. This paper examines three dimensions of this problem—the psychology of algorithmic trust, the mechanistic interpretability program’s assumptions about alignment, and the specific failure modes of interpretability in high-stakes domains like security and healthcare—and concludes that trust in ML systems requires not just explanation, but demonstrated value alignment and robust performance under adversarial conditions. The practical implication is stark: interpretability research should stop treating explanation as a proxy for trustworthiness and instead focus on building systems whose behavior is trustworthy, with interpretability as a secondary tool for post-hoc auditing and failure analysis.
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