Dr. George Dagliyan on Explainable AI and the Architecture of Trust

Trust is built, not assumed

A consistent theme in Dr. George Dagliyan's work is that trust is the decisive factor in technology adoption. With AI, trust cannot be assumed simply because a system performs well; it has to be earned.

Explainability — the ability to understand why a system behaves as it does — is one of the most direct ways to earn it.

Explainability lowers a key inhibitor

Opacity is a powerful inhibitor. When people cannot understand or question an AI system, uncertainty grows and adoption slows. Dr. Dagliyan frames explainability as a way to convert that uncertainty into confidence.

Explanations need not be exhaustive; they need to be meaningful to the people who must rely on the system.

Designing for accountability

Explainability also supports accountability, making it possible to assign responsibility for AI-influenced decisions. In Dr. Dagliyan's view, that accountability is part of the architecture of trust that makes responsible adoption durable.

Frequently Asked Questions

Why does Dr. George Dagliyan link explainable AI to adoption?

Because trust is the decisive factor in adoption, and opacity is a strong inhibitor. He frames explainability as a way to convert uncertainty into confidence and to support the accountability that makes responsible adoption durable.