docs: fix fabricated ~20.9pp hiring gap in three explainers - #564
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…rate-treatment / neural-networks All three explainers cited a "~20.9 percentage point" gender gap for the AI Fair Recruitment audit. 20.9% is README.md's pull-quote for the *relative* reduction ((21.62 - 17.10) / 21.62 = 20.9%), not an absolute pp gap. The real absolute gap - per README.md's canonical table and matched correctly by disparate-impact.md and supervised-learning.md - is 21.62% vs 17.10%, a 4.51pp gap, closing to 0.12pp (97.3% relative reduction) after dropping gender and age. - demographic-parity.md: replaced the invented ~71%/~50% biased rates and ~67%/~67% fixed rates with the canonical 21.62%/17.10% and a prose statement of the 0.12pp / 97.3% result. - disparate-treatment.md: "20.9pp hire rate gap" -> "4.51pp hire rate gap (21.62% vs 17.10%)". - neural-networks.md: replaced the invented 61.2%/40.3% biased rates, 54.1%/54.0% fixed rates, and the "20.9% -> ~18% -> 0.1% (99.5%)" summary with the canonical 21.62%/17.10%, 4.51pp -> 0.12pp, 97.3% figures. The fabricated post-fix per-group rates (no canonical source) are dropped rather than replaced with new guesses. Regenerated the pages, package mirror, and llms-full.txt. Closes yakew7#550
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September 9, 2026 20:18
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@yakew7 @Shreyash0712 - new PR touching a path you own, please review. |
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Problem
demographic-parity.md,disparate-treatment.md, andneural-networks.mdall present a "~20.9 percentage point" gender gap for the AI Fair Recruitment audit.20.9%isREADME.md's pull-quote for the relative reduction:(21.62 - 17.10) / 21.62 = 20.9%. It is not an absolute pp gap. The real absolute gap - perREADME.md's canonical results table, and cited correctly bydisparate-impact.mdandsupervised-learning.md- is 21.62% vs 17.10%, a 4.51pp gap, closing to 0.12pp (97.3% relative reduction) after dropping gender and age.demographic-parity.mdandneural-networks.mdadditionally invent their own per-group hire rates (~71%/~50%,61.2%/40.3%,54.1%/54.0%,~67%/~67%) that appear nowhere else in the repo, andneural-networks.md's summary table has a fabricated99.5%reduction.Fix
Every AI Fair Recruitment number in the three files is now the canonical set (
21.62%/17.10%/4.51pp->0.12pp/97.3%). The fabricated post-fix per-group rates - which have no canonical source in the repo (the audits only produce the gap, not fixed per-group rates) - are dropped rather than replaced with new guesses. Pages,faircode/_explainers/mirror, andllms-full.txtregenerated.check_em_dash.py,check_broken_links.py,check_generated_files_current.pypass.Closes #550