docs: fix two Results-at-a-Glance inaccuracies (COMPAS reduction %, Benefits Denial proxy count)

#545: COMPAS's "Reduction" figure used the raw percentage-point gap
(86.77 - 15.69 = 71.08 -> "71%") instead of the relative reduction
(86.77 - 15.69) / 86.77 = 81.9% -> "82%") that every other row in the
same table uses. Fixed in COMPAS/README.md, the root README's
Results-at-a-Glance row, and the "Result: N% reduction" line in the
root README's COMPAS section. This understated COMPAS's actual
mitigation effectiveness.

#546: Benefits Denial's audit.yaml lists five proxy_features
(marital.status, occupation, relationship, hours.per.week, fnlwgt) and
fair.py removes all five with documented reasoning, but both READMEs
listed only four, omitting fnlwgt. Added fnlwgt to the proxy list in
Benefits Denial/README.md and the root README (summary-table row,
"five proxy variables" counts, and the fair.py code-block comment list).

Closes #545
Closes #546

Author: propcgamer20-png

Benefits Denial/README.md

@@ -19,7 +19,7 @@ python3 "Benefits Denial/fair.py"     # mitigated (protected attribute + proxies
 ## What the audit controls
 
 - Protected attribute(s): Sex, Race, Origin, Age
-- Proxy feature(s) removed in `fair.py`: Relationship, Marital Status, Hours, Occupation
+- Proxy feature(s) removed in `fair.py`: Relationship, Marital Status, Hours, Occupation, fnlwgt (census sampling weight)
 - Fairness metric: Demographic Parity (difference in positive-prediction rate between groups)
 
 ## Expected result (published, paper-aligned)

COMPAS/README.md

@@ -26,6 +26,6 @@ python3 "COMPAS/fair.py"     # mitigated (protected attribute + proxies dropped)
 
 | Group | Gap, biased (`unfair.py`) | Gap, mitigated (`fair.py`) | Reduction |
 |-------|--------------------------:|---------------------------:|----------:|
-| Race | 86.77% | 15.69% | 71% |
+| Race | 86.77% | 15.69% | 82% |
 
 These match the "Results at a Glance" table in the [main README](../README.md#results-at-a-glance) and the frozen snapshot in `paper/results-frozen/`. The scripts are deterministic at `random_state=42`, so a correct local run reproduces them exactly. If your numbers differ, check the seed, the split, and your package versions before opening an issue - and never edit the frozen numbers to match a local run (see [CLAUDE.md](../CLAUDE.md)).

README.md

@@ -68,12 +68,12 @@ Each audit ships as both a pair of Python scripts (`unfair.py` / `fair.py`) for
 
 | # | Domain | Protected Attribute | Proxies Removed | Gap Before → After | Reduction |
 |:-:|--------|--------------------|-----------------|--------------------|:---------:|
-| 01 | [Criminal Justice](#01--compas--criminal-justice-bias) | Race | Custody Status | 86.77% → 15.69% | **71%** |
+| 01 | [Criminal Justice](#01--compas--criminal-justice-bias) | Race | Custody Status | 86.77% → 15.69% | **82%** |
 | 02 | [Hiring](#02--ai-fair-recruitment--hiring-bias) | Gender | Age | 4.51% → 0.12% | **97.3%** |
 | 03 | [Lending](#03--german-credit-lending--lending-bias) | Age | Employment Tenure | 7.16% → 1.89% | **73.6%** |
 | 04 | [Healthcare](#04--insurance-denial--healthcare-bias) | Age, Gender | BMI, Smoker, Diabetic | Age: 7.93% → 3.18% | **60%** |
 | ↳  | | | | Gender: 5.44% → 1.54% | **72%** |
-| 05 | [Welfare](#05--benefits-denial--welfare-eligibility-bias) | Sex, Race, Origin, Age | Relationship, Marital Status, Hours, Occupation | Sex: 18.00% → 8.52% | **53%** |
+| 05 | [Welfare](#05--benefits-denial--welfare-eligibility-bias) | Sex, Race, Origin, Age | Relationship, Marital Status, Hours, Occupation, fnlwgt | Sex: 18.00% → 8.52% | **53%** |
 | ↳  | | | | Race: 12.75% → 6.90% | **46%** |
 | ↳  | | | | Origin: 4.40% → 0.52% | **88%** |
 | 06 | [Healthcare Readmission](#06--healthcare-readmission--clinical-bias) | Race, Gender, Age | Payer Code, Discharge Disposition, Medical Specialty, Prior Inpatient | Gender: 0.02% → 0.04% | **+100% ↑** |
@@ -371,7 +371,7 @@ X = pd.get_dummies(df[[
 | White Defendants | 69.02% |
 | **New Fairness Gap** | **15.69%** |
 
-**Result: 71% reduction in the fairness gap.**
+**Result: 82% reduction in the fairness gap.**
 
 > **Key insight:** Removing race alone isn't enough. Proxy variables like custody status carry the same racial signal because of historical over-policing of Black communities. Both the protected attribute *and* its proxies must be removed.
 
@@ -546,7 +546,7 @@ Automated welfare and benefits systems use income-prediction models to screen ap
 
 #### The Problem - `unfair.py`
 
-Trained with sex, race, age, and national origin directly, plus four proxy variables that reconstruct those attributes even after the protected columns are removed.
+Trained with sex, race, age, and national origin directly, plus five proxy variables that reconstruct those attributes even after the protected columns are removed.
 
 | Group | Ineligibility Flag Rate |
 |-------|:-----------------------:|
@@ -568,7 +568,7 @@ Trained with sex, race, age, and national origin directly, plus four proxy varia
 
 #### The Fix - `fair.py`
 
-Dropped all four protected attributes and all four proxy variables. Retained only the features a means-tested programme can legitimately consult under equality law.
+Dropped all four protected attributes and all five proxy variables. Retained only the features a means-tested programme can legitimately consult under equality law.
 
 ```python
 # THE FIX: Policy-defined economic signals only
@@ -586,6 +586,7 @@ features = [
     # marital.status removed ✓  (proxy: encodes sex via spousal status)
     # hours.per.week removed ✓  (proxy: encodes sex via caregiving gap)
     # occupation     removed ✓  (proxy: encodes race via occupational segregation)
+    # fnlwgt         removed ✓  (proxy: census sampling weight, no causal link)
 ]
 ```