docs: fix counterfactual-fairness.md's COMPAS numbers to the repo-canonical figures - #561
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yakew7 merged 1 commit intoSep 10, 2026
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…onical figures The COMPAS "biased model" table cited Black 58.15% / White 28.37% / 29.78pp gap. Those numbers appear nowhere else in the repo. The canonical unfair.py COMPAS result, cited identically by README.md and 8+ other explainers, is Black 87.16% / White 0.40% / 86.77pp. Replaced the table with the canonical figures (and matched the "High-Risk Flag Rate" header wording used elsewhere). The surrounding causal-graph narrative (CustodyStatus proxy, prior arrests downstream of race, why dropping race alone didn't help) is unchanged and holds identically. Regenerated the page, package mirror, and llms-full.txt. Closes yakew7#551
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September 9, 2026 20:11
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@yakew7 @Shreyash0712 - new PR touching a path you own, please review. |
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Problem
explainers/counterfactual-fairness.md's COMPAS "biased model" table cited:Those three numbers appear nowhere else in the repo. The canonical
unfair.pyCOMPAS result - cited identically byREADME.mdand at least 8 other explainers (automation-bias,confounding-variable,data-leakage,how-ai-detects-patterns,ml-bias,proxy-variables,ai-objectivity-myth, ...) - is Black 87.16% / White 0.40% / 86.77pp.Fix
Replaced the table with the canonical figures and matched the "High-Risk Flag Rate" header wording used elsewhere. The surrounding causal-graph narrative (CustodyStatus as a race proxy, prior arrests being causally downstream of race, why removing race alone didn't remove the bias) does not cite the numbers again and holds identically - if anything the real 86.77pp gap makes the point more sharply. Regenerated
counterfactual-fairness.html, thefaircode/_explainers/mirror, andllms-full.txt.check_em_dash.py,check_broken_links.py,check_generated_files_current.pypass.Closes #551