Municipal Social Benefits Allocation Disparity and Statistical Fairness Audit Checklist

Role: Lead Quantitative Policy Analyst & Algorithmic Equity Auditor

Context

• Benefit Program: {{program_entitlement_name}}
• Protected Attributes: {{protected_demographic_classes}}
• Cutoff Threshold: {{decision_rule_threshold}}
• Historical Baseline: {{historical_acceptance_rates}}
• Equity Formalism: {{fairness_metric_criteria}}
• Bias Risks: {{data_censoring_risks}}

Task

Construct a comprehensive, rigorous statistical fairness checklist to evaluate the eligibility scoring algorithm for {{program_entitlement_name}}, ensuring compliance with equity requirements across {{protected_demographic_classes}}.

Method

1. Disaggregate applicant distribution and scoring outcomes across {{protected_demographic_classes}}.
2. Quantify disparate impact ratios against baseline rates defined in {{historical_acceptance_rates}} (e.g., four-fifths rule).
3. Test the chosen {{fairness_metric_criteria}} (e.g., false negative rate parity across sub-groups).
4. Evaluate sensitivity of the classification cutoff at {{decision_rule_threshold}} across demographic strata.
5. Audit feature importance to identify unapproved proxy variables correlated with protected attributes.
6. Investigate survivor bias, unobserved selection effects, and {{data_censoring_risks}} in the training data.
7. Compute intersectional disparity metrics combining multiple protected characteristics simultaneously.
8. Establish continuous post-deployment statistical drift monitoring protocols.

Constraints

• Checklist MUST enforce concrete statistical disparity tolerances (e.g., adverse impact ratio limits).
• MUST NOT permit deployment if false negative disparities for vulnerable populations exceed stated safety limits.
• Must provide explicit guidance for trade-offs between predictive accuracy and demographic fairness.
• Use clear tabular format requirements for diagnostic documentation.

Output format

• Structured markdown quality checklist with five functional categories:
  • Phase 1: Input Data & Proxy Variable Screening (4-5 checklist items)
  • Phase 2: Statistical Parity & Error Rate Disparity Checks (5-6 checklist items)
  • Phase 3: Threshold Sensitivity & Frontier Analysis (3-4 checklist items)
  • Phase 4: Long-term Monitoring & Drift Detection (3-4 checklist items)
• Concluding section: Algorithmic Equity Determination Matrix with quantitative thresholds and remediation actions.

Self-review

1. Are all protected groups in {{protected_demographic_classes}} specifically evaluated in the error-rate checks?
2. Does the checklist explicitly address the specific metrics defined in {{fairness_metric_criteria}}?
3. Are proxy leakage and {{data_censoring_risks}} covered in the pre-deployment tests?