AI & Machine Learning
Auditing Subgroup Fairness
Audit an OpenMed NER or de-identification model for performance disparities across demographic subgroups (sex, age band, race/ethnicity when available) using openmed.eval.fairness_report. Use when the user wants per-subgroup recall and leakage, wants to check whether de-identification under-protects a group, wants to surface a documentation gap where subgroup data is missing, or needs equalized-odds-style disparity numbers for a clinical model. Trigger on \"fairness\", \"subgroup\", \"bias audit\", \"disparity\", \"equalized odds\", \"under-protected group\", \"per-group recall\", or \"STANDING Together\" for an OpenMed model.
- Publisher / source
- maziyarpanahi/openmed (found on GitHub top repositories)
- Upstream record
- maziyarpanahi/openmed
- Version
- Version 1
- Published
- Publication review
- Approved for publication
- Content fingerprint
07f38d8255bc3c7cb28888d38f415990a12c2f03e1b8355fdf5241d883e25335