condfair: An R Package for Ability-Conditioned Fairness and Explanation Diagnostics in Automated Scoring

Fairness in automated scoring is typically evaluated with a single global statistic contrasting a focal and reference group-an approach that can either mask a disparity that changes sign across the ability range, or overstate one by conflating it with genuine ability differences between groups (impact). We introduce condfair, an R package that adapts differential item functioning (DIF) logic to automated scoring: it estimates a conditional disparity function across ability levels, tests it with a wild-bootstrap omnibus procedure, decomposes bias into uniform and non-uniform components, and identifies candidate feature-level sources of a detected disparity via conditional SHAP disparity testing. Using the PERSUADE 2.0 essay corpus, we show a global measure can conceal a large, ability-concentrated gender disparity (marginal gap = 0.003; peak conditional disparity = 0.276, p = .001) while overstating an English Language Learner disparity by conflating it with impact (marginal gap = 0.280; conditional bias = 0.045).