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Khoury College of Computer Sciences, Northeastern University
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Achieving fairness in machine learning may come at a lower data cost than previously thought, as this framework balances bias mitigation with representation needs.
Models may appear to improve while actually worsening their ability to screen out defaulters, revealing a critical flaw in standard evaluation practices.
