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Found 2 papers, 1 papers with code
Fair and Optimal Classification via Post-Processing
To mitigate the bias exhibited by machine learning models, fairness criteria can be integrated into the training process to ensure fair treatment across all demographics, but it often comes at the expense of model performance.
FOCUS: Fairness via Agent-Awareness for Federated Learning on Heterogeneous Data
However, due to the heterogeneous nature of local data, it is challenging to optimize or even define fairness of the trained global model for the agents.
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