Create explainer for Proxy Entanglement

[yakew7]

Add explainer on proxy entanglement in datasets and its implications for fairness in models.

Summary

Type

• Audit
• Explainer
• Bug fix
• Other

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Audit checklist

• I opened or linked a corresponding issue first
• The folder is named after the domain, not the dataset
• unfair.py includes protected attributes and prints the required output format
• fair.py removes protected attributes and identified proxy variables
• Both scripts use random_state=42 and an 80/20 train/test split
• Proxy variables were actually tested, not just guessed
• unfair.png and fair.png are included as PNG screenshots
• The dataset is public and accessible without login or payment
• The dataset file is included, or DATA.md is included if the file is too large
• README.md includes the new results row and audit section
• A notebook was added if the audit benefits from one

Before fairness gap:

After fairness gap:

Reduction:

Protected attribute(s):

Proxy variables dropped:

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Explainer checklist

• The file is in explainers/ and uses lowercase hyphenated naming
• It includes a plain-language definition
• It uses a real example from this repo or a documented real-world case
• It includes runnable Python detection or measurement code
• It acknowledges limitations or trade-offs
• It links to related explainers or repo projects
• It includes 2-3 primary sources
• The Explainers table in README.md was updated

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Linked issue

Closes #

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Project	Deployment	Actions	Updated (UTC)
fair-code	Ready	Preview, Comment	Jun 9, 2026 8:00am