Algorithmic Audit

A structured evaluation of an algorithmic system that measures how its behaviour differs across users, groups, or contexts - typically to surface bias, fairness failures, or disparate impact. Accessibility-oriented audits go beyond aggregate accuracy to look at where and why a model fails for disabled users, using metrics like per-group accuracy, false-rejection rates, or perception gaps. Effective audits combine quantitative disparity measurement with qualitative evidence (user accounts, interaction traces) and produce actionable recommendations for data, model, and interface changes.