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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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Knowledge check

5 questions from this module's lessons.

0/5 correct
  1. 01

    When designing interfaces for probabilistic AI as opposed to deterministic software, what is a primary goal for maintaining user trust?

  2. 02

    According to the lesson on Algorithmic Bias, who is considered the 'final line of defense' against exclusion in AI products?

  3. 03

    What is the primary objective of implementing Explainable AI (XAI) frameworks?

  4. 04

    What is the common reason for the failure of 85% of AI projects according to global standards research?

  5. 05

    Which design philosophy is suggested for regulatory compliance to protect a company's reputation while accelerating adoption?

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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)
101Male9021085ApprovedHigh Income
102Female1000142RejectedZip Code Risk
1031000138RejectedZip Code Risk
104Female6060192ApprovedCredit History
105Male1000140RejectedZip Code Risk
106Female9411078ApprovedCredit History
107Male1000145RejectedZip Code Risk
1083030381ApprovedHigh Income
109Female9810188ApprovedCredit History
110Male7330155RejectedInsufficient History