Lesson 245 of 1550
AI Credit Decisioning Fairness: What Auditors Are Actually Looking For
Bank regulators expect AI credit models to demonstrate fairness across protected classes. The audit isn't 'is the model accurate?' — it's 'is it accurate equitably?'
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What this lesson covers
Learning path
The main moves in order
- 1The premise
- 2fair lending
- 3ECOA
- 4disparate impact
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Section 1
The premise
Regulator audits of AI credit models focus on fairness, not just accuracy; fair-lending compliance requires evidence the model serves protected classes equitably.
What AI does well here
- Document fair-lending testing methodology before deployment (not after the audit notice)
- Run disparate-impact analysis across protected classes with documented thresholds
- Implement explainability for adverse-action notices (every denial needs a reason that's not 'model said no')
- Maintain ongoing monitoring — fair-lending compliance isn't a deployment-time check, it's continuous
What AI cannot do
- Substitute for legal and compliance review of the fair-lending program
- Make the model fair without addressing data sources that may encode bias
- Replace the human review process for borderline cases
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