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Module 05 · UX and Responsible AI
Designing a Responsible AI Credit Scoring Interface
You are the AI Product Manager for 'GlobalPay,' a fintech startup. Your new credit-scoring AI just rejected a loyal customer, and you must design the 'Rejection Experience' to be fair, explainable, and compliant with the GDPR 'Right to Explanation' while avoiding a 'Black Box' feel.
45 minBeginner 3 outcomes 7 steps · 3 checkpoints
lab progress0/10 · 0%
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Dataset
credit_audit_logs.csv
10 recent credit applications including applicant demographics, loan status, and the 'Top Feature' the AI used to make the decision.
Includes intentional proxy variables (Zip Code) and missing data ('null' gender) representing sampling/data entry bias.
| applicant_id(integer) | gender(string) | zip_code(string) | ai_score(integer) | decision(string) | primary_reason(string) |
|---|---|---|---|---|---|
| 101 | Male | 90210 | 85 | Approved | High Income |
| 102 | Female | 10001 | 42 | Rejected | Zip Code Risk |
| 103 | 10001 | 38 | Rejected | Zip Code Risk | |
| 104 | Female | 60601 | 92 | Approved | Credit History |
| 105 | Male | 10001 | 40 | Rejected | Zip Code Risk |
| 106 | Female | 94110 | 78 | Approved | Credit History |
| 107 | Male | 10001 | 45 | Rejected | Zip Code Risk |
| 108 | 30303 | 81 | Approved | High Income | |
| 109 | Female | 98101 | 88 | Approved | Credit History |
| 110 | Male | 73301 | 55 | Rejected | Insufficient History |