What are some common metrics used to assess the fairness of machine learning models?

May 31, 2022 ... Additionally, ML algorithms tend to overfit to the data on which they are trained, which entails the learning of spurious relationships present ... Jun 8, 2023 ... Bias mitigation in machine learning models · 1. Pre-processing algorithms · 2. In-processing algorithms · 3. Post-processing algorithms. Aug 18, 2021 ... We demonstrate that state-of-the-art scheduling systems cause the black patients in our data set to wait about 30% longer than nonblack patients. In statistics and machine learning, the bias–variance tradeoff describes the relationship between a model's complexity, the accuracy of its predictions, ... Mar 26, 2018 ... In the case of self-learning systems, the type of “garbage” is biased data. Left unchecked, feeding biased data to self-learning systems can ... Jul 28, 2022 ... Long Story Short: Omitted Variable Bias in Causal Machine Learning ... We derive general, yet simple, sharp bounds on the size of the omitted ... Sep 11, 2024 ... Each chapter addresses a different topic and real-world case studies are featured throughout that highlight discriminatory machine learning ... This manuscript is a literature study that provides a detailed survey regarding the different categories of bias and the corresponding approaches. Feb 7, 2022 ... We present a guide to the data ecosystem used by health insurers to highlight where bias can arise along machine learning pipelines. We suggest ... Dec 11, 2018 ... As machine learning systems are used more and more to make decisions about insurance, criminal justice, credit, and child welfare, ...