<ul><li>The study focuses on the tradeoffs between accuracy, fairness, and utility in machine learning algorithms.</li><li>Algorithms proposed in this context achieve evidence-based fairness by supporting classification and ranking techniques preserving accurate subpopulation classification rates.</li><li>The research presents impossibility results indicating the challenge of simultaneously achieving accurate classification rates and optimal loss minimization in some cases.</li><li>The study highlights the computational challenges in learning a good approximation of the Bayes-optimal predictor, presenting a choice between accuracy and fairness.</li></ul>

Accuracy vs. Accuracy: Computational Tradeoffs Between Classification Rates and Utility

Discover more