<ul><li>A new framework for fairness adjustments in machine learning models has been introduced in a recent paper on arXiv.</li><li>The framework applies to various machine learning tasks, including regression and classification, and supports different fairness metrics.</li><li>Unlike traditional approaches, this method adapts in-processing techniques for post-processing, providing greater flexibility in model development.</li><li>The advantages of this framework include preserving model performance, eliminating the need for custom loss functions, accommodating black-box systems, and providing interpretable insights.</li></ul>

General Post-Processing Framework for Fairness Adjustment of Machine Learning Models

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