AINeutralarXiv – CS AI · Jun 106/10
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Is Fairness Truly Fair? Towards Reliable Lipschitz Fairness in Multi-Task Learning via Fixed-\texorpdfstring{$\delta$}{delta} Alignment
Researchers propose ReLiF, a framework addressing fairness evaluation problems in multi-task machine learning by using fixed evaluation thresholds rather than model-dependent ones. The work identifies how different algorithms can appear unfairly comparable under inconsistent fairness metrics and demonstrates that proper auditing protocols reveal genuine utility-fairness trade-offs obscured by conventional methods.
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