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Lab Begins Evaluation of Explainable AI Workflows

Jul 24, 2026

Marcus Hale

The lab is testing new evaluation workflows that help researchers compare explanations, confidence, and expert feedback in one process.

The workflow gives reviewers a structured way to compare model explanations against the evidence they already use. Instead of treating explanation quality as a single score, the team is studying how explanations change expert behavior.

Evaluating Explanations in Practice

The evaluation examines whether popular explanation techniques actually help domain experts make better calls in day-to-day workflows.

Study Design

Experts review model recommendations with and without explanations, while the team measures decision quality, speed, and confidence.

What Counts as a Good Explanation

Early sessions suggest that usefulness depends less on algorithmic detail and more on whether an explanation matches how experts already reason.

The initial study will compare interface prototypes across several applied research scenarios.

Results will guide future work on evidence presentation, confidence communication, and human-in-the-loop evaluation.

Experts review model recommendations with and without explanations while decision quality, speed, and confidence are measured.
Early sessions suggest good explanations match expert reasoning rather than exposing algorithmic detail.