AI Bias Audits for Court Systems: Working Template

AI bias audits are increasingly required for court systems using AI.

Why bias audits

Court AI systems must comply with:

• Equal protection (14th Amendment)
• Due process (5th/14th Amendment)
• Title VI (federal funding recipients)
• ABA Formal Opinion 512 + 533

Working audit framework

1. Data audit

• Source data composition
• Demographic representation
• Label quality

Tools: Pandas, Great Expectations, Aequitas, AI Fairness 360.

2. Outcome audit

• Demographic parity
• Equal opportunity
• Predictive parity

Statistical tests:

• 4/5ths rule
• Chi-square
• Fisher exact
• Bootstrap CI

3. Counterfactual audit

• Demographic substitution
• Outcome consistency

Tools: AIF360, Fairlearn.

4. Calibration audit

• Predicted probability vs. observed frequency
• Calibration by demographic group

5. Operational audit

• Disparate impact in deployment
• Access patterns by demographic
• Outcome tracking

Working cadence

• Pre-deployment: full audit
• Quarterly: outcome audit
• Annually: full audit
• After model update: targeted audit

Acknowledgments

NIST AI RMF 1.0, ABA Formal Opinion 512.

Dillon Deutsch has built AI bias audit frameworks. https://courtgpt.ai