Overview
What this challenge is about.
Audit bias in a fintech credit model using three fairness metrics and build a governance playbook. Earn a verifiable certificate.
The scenario
Zilch is a UK BNPL fintech with ~4M users operating under FCA supervision and increasing regulatory scrutiny on algorithmic consumer lending decisions.
The Brief
What you'll do, and what you'll demonstrate.
Does Zilch's credit scoring model produce ethically and legally defensible outcomes across protected groups, and what governance controls should follow?
Earning criteria — what you'll demonstrate
- Apply quantitative fairness metrics to evaluate AI systems for disparate impact
- Translate ethical concerns into regulatory and governance requirements
- Critically assess trade-offs between competing fairness definitions
- Communicate AI risk findings to non-technical executive audiences
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
AI, Ethics and Society
Master · Ai In Business
Strong alignment
This challenge maps to AI, Ethics and Society at the Master level. It sharpens the same practical skills your coursework expects — but in a real industry context with actual constraints and deliverables.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
- Algorithmic Fairness
Apply algorithmic fairness to solve real industry problems and demonstrate production-level capability.
- Ai Audit
Apply ai audit to solve real industry problems and demonstrate production-level capability.
- Regulatory Analysis
Apply regulatory analysis to solve real industry problems and demonstrate production-level capability.
- Data Analysis
Analyze real datasets, build models, and communicate findings that drive decisions.
- Ai Governance
Apply ai governance to solve real industry problems and demonstrate production-level capability.
Careers
Career paths this challenge builds toward
Completing this challenge demonstrates skills that transfer directly to these roles: