Module 05 · Bias Auditing & Transparency

Conducting a Bias Audit and Transparency Report for Credit Limit Increases

You are a Product Manager at 'FinEdge.' Your automated credit limit increase system is under review after reports of bias. You need to verify if the 4/5ths Rule is being met for gender, identify data points acting as proxies for protected groups, and create a Model Card to communicate findings to your Chief Compliance Officer.

75 minIntermediate 3 outcomes 8 steps · 3 checkpoints

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Dataset

finedge_audit_data.csv

20 credit limit increase decisions with applicant_id, gender, zip_code, debt_to_income, and status (Approved/Denied).

Male approval rate: 9/10 = 90%. Female approval rate: 2/10 = 20%. Impact ratio: 0.22. ZIP 10001 skews Female and Denied.

applicant_id(integer)	gender(string)	zip_code(string)	debt_to_income(number)	status(string)
1	Male	20002	0.22	Approved
2	Male	30005	0.18	Approved
3	Male	20002	0.31	Approved
4	Male	40010	0.27	Approved
5	Male	30005	0.2	Approved
6	Male	20002	0.35	Approved
7	Male	50020	0.24	Approved
8	Male	40010	0.29	Approved
9	Male	30005	0.33	Approved
10	Male	20002	0.55	Denied
11	Female	10001	0.28	Denied
12	Female	10001	0.31	Denied
13	Female	10001	0.22	Approved
14	Female	10001	0.34	Denied
15	Female	10001	0.4	Denied
16	Female	10001	0.26	Denied
17	Female	20002	0.19	Approved
18	Female	10001	0.37	Denied
19	Female	10001	0.3	Denied
20	Female	10001	0.42	Denied