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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.
Getting started
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Package details |
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| Author | Subir Hait [aut, cre] (ORCID: <https://orcid.org/0009-0004-9871-9677>) |
| Maintainer | Subir Hait <haitsubi@msu.edu> |
| License | GPL (>= 3) |
| Version | 0.1.0 |
| URL | https://github.com/causalfragility-lab/aiDIF |
| Package repository | View on CRAN |
| Installation |
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