How can adversarial learning be used for bias mitigation?

Mar 3, 2021 ... Abstract. Western societies are marked by diverse and extensive biases and inequality that are unavoidably embedded in the data used to train ... 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. Jan 31, 2018 ... This post assumes some familiarity with machine learning, and reinforcement learning in particular. If you are new to RL, I'd recommend ... Model bias, in Machine Learning, refers to the presence of systematic errors in a model that can cause it to consistently make incorrect predictions. Oct 14, 2020 ... The result of this technique is an RF bias-correction model that, when applied to SNODAS, offers improved daily estimates of SWE which can then ... Feb 10, 2021 ... The application of machine learning (ML) technologies in medicine generally but also in radiology more specifically is hoped to improve ... Sep 11, 2024 ... Each chapter addresses a different topic and real-world case studies are featured throughout that highlight discriminatory machine learning ... According to some futurists, financial markets' automation will substitute increasingly sophisticated, objective, analytical, model-based assessments of, for ... Dec 11, 2018 ... As machine learning systems are used more and more to make decisions about insurance, criminal justice, credit, and child welfare, ... Feb 15, 2023 ... Conclusions. Our study illustrates the racial bias that can result when creating ML models without proper bias mitigation. Healthcare-related ML ...