Public Bias Audit
Composite × Protected-Class Correlation Audit
Quarterly statistical check that ValiFit composite scores do not systematically correlate with Census protected-class indicators. Methodology + threshold are public; the dataset is published as Schema.org JSON-LD for external verification.
First quarterly audit pending
No bias audit has run yet. The first quarterly audit is scheduled. See /trust/charters/public-bias-audit for the methodology.
Read the Public Bias Audit charter →Methodology
- Sample. 1% deterministic stratified sample of US Census tracts (capped at 5,000 tracts). Reproducible across runs via deterministic ordering on tract_id hash.
- Composites. 8 published composite scores (Budget, Commute, Appreciation, School Investment, Environmental Safety, Safety Infrastructure, Health Infrastructure, Livability).
- Proxies. 6 Census protected-class indicators at tract level: % Black, % Hispanic, % Asian, % age 65+, % under 18, % with disability.
- Statistics. Pearson and Spearman correlations on aligned (composite, proxy) tract-level data, n ≥ 30 per cell.
- Threshold.|Pearson r| > 0.30 = flagged. Anomaly count > 0 ⇒ product review required.
- Publication. Full matrix + dataset JSON-LD on this page. Audit history retained in valifit.bias_audits.
Aligns with
- HUD Disparate Impact Standard (24 CFR 100.500)
- NIST AI Risk Management Framework
- EU AI Act Article 27 — Post-deployment monitoring of high-risk AI systems