Introduction

Loan approval algorithms can exhibit bias: denying loans disproportionately to protected classes (race, gender). Measuring bias rigorously—using fairness metrics—enables detection and mitigation. Multiple fairness definitions exist; choosing appropriate metrics for context is critical.

Fairness Metrics

Demographic parity: approval rates equal across groups. Equalized odds: true positive rates (approvals of qualified applicants) equal across groups. Predictive parity: false positive rates equal. Each metric captures different fairness notion; trade-offs exist (cannot always satisfy all simultaneously).

Bias Detection

Compute fairness metrics on historical loan decisions. Identify disparities: if approval rate for Group A (80%) differs significantly from Group B (60%), model exhibits disparate impact. Statistical testing determines significance (controlling for confounders).

Mitigation

If bias detected, consider: (1) Remove protected class features from model (limited effect if proxy features exist); (2) Adjust decision thresholds across groups (explicit fairness constraints); (3) Augment training data for underrepresented groups; (4) Use fair ML algorithms (built-in fairness constraints). Trade-off: fairness interventions typically reduce model accuracy; optimize fairness-accuracy frontier.

Conclusion

Systematic bias measurement and mitigation in loan algorithms improves fairness and reduces regulatory/legal risk.