Trust and Adoption

People often say explainability builds trust. That goal needs one more word: calibrated trust.

A conservation scientist should rely on a whale detector where it works and slow down where it struggles. Blind trust creates automation bias. Blanket distrust leaves useful detections sitting in a queue. The goal is reliance that rises and falls with actual capability.

An explanation can help when it shows evidence a domain expert recognizes. If a whale detector highlights the fluke or body contour, the scientist gains a concrete reason to inspect the result. If it highlights a boat wake, the same explanation should reduce trust.

Presentation quality can create its own failure. A smooth narrative, confident language, or polished visualization may persuade users even when the explanation has weak fidelity to the model. A useful interface makes uncertainty and limitations visible.

Adoption also depends on workflow. Ecologists may reject a tool because earlier systems performed poorly, the model does not fit field practice, or the cost of checking its mistakes exceeds the time it saves. Explainability contributes when it helps experts validate cases, discover failure modes, and decide where automation belongs.