aiDIF: Differential Item Functioning for AI-Scored Assessments
Detects and quantifies differential item functioning (DIF) in
AI-scored educational and psychological assessments. Provides a fully
self-contained robust DIF engine (M-estimation via iteratively
re-weighted least squares with the bi-square loss) alongside the novel
Differential AI Scoring Bias (DASB) test, which detects item-level
scoring shifts that differ across subgroups when comparing human and AI
scoring conditions. Includes simulation utilities, anchor weight
diagnostics, and an AI-effect classification framework.
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