How can we detect and mitigate selection bias in machine learning?

Oct 25, 2019 ... Machine learning systems disregard variables that do not accurately predict outcomes (in the data available to them). This is in contrast to ... Jul 13, 2021 ... In this survey, we investigated different real-world applications that have shown biases in various ways, and we listed different sources of ... Feb 15, 2023 ... Conclusions. Our study illustrates the racial bias that can result when creating ML models without proper bias mitigation. Healthcare-related ML ... Feb 4, 2019 ... Bias can creep in at many stages of the deep-learning process, and the standard practices in computer science aren't designed to detect it. This paper postulates that the root causes of bias are the prior decisions that affect- (a) what data was selected and (b) the labels assigned to those ... Jul 12, 2023 ... Healthcare algorithms and AI bias can contribute to existing health disparities for certain populations based on race, ethnicity, gender, age, or other ... Mar 20, 2023 ... Debiasing ML refers to the process of mitigating or removing the effects of bias from the model or the data used to train the model. Jul 24, 2024 ... Introduction: Machine learning (ML) algorithms have been heralded as promising solutions to the realization of assistive systems in digital ... Oct 18, 2022 ... We develop and evaluate a machine learning system for identifying five major categories of biased and discriminatory language in job advertisements. 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.