Author

Zeyang Kong

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Open access 2026

A Quantitative and Qualitative Analysis of Data Selection Impact on Machine Learning Fairness and Utility

The results show that ML data selection can hurt model fairness in a non-negligible number of cases, and compromise model utility in more than half of the cases, and provide interesting research directions for utility- and fairness-aware ML data selection.

Nawel Benarba, Zeyang Kong, Sara Bouchenak · IEEE Access · 1 citation