How does bias in AI affect different demographic groups?

Tackling issues of bias and fairness when building and deploying machine learning and data science systems has received increased attention from the research ... Aug 24, 2022 ... This report presents 12 suboptimal practices during data handling of an ML study, explains how those practices can lead to biases, and describes what may be ... Both of these existing methods address the problem of bias by adjusting the machine learning model. However, oftentimes it is the training data itself – i.e., ... Feb 10, 2021 ... The application of machine learning (ML) technologies in medicine generally but also in radiology more specifically is hoped to improve ... Feb 8, 2024 ... We developed a unified transformer-based machine learning framework ImmuBPI that comprised three tasks and achieved state-of-the-art performance. 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 ... Dec 28, 2018 ... In this paper, we reconcile the classical understanding and the modern practice within a unified performance curve. This "double descent" curve ... Mar 1, 2021 ... "Bias" is used to characterize the model error due to simplified assumptions of the model, eg, using linear regression when linear regression is not complex ... Nov 10, 2017 ... Machine learning can perpetuate and even amplify behavioral biases. By design, a social-media site filtering news based on user preferences ... The purpose of this study is to illustrate the harmful effects of learned human-like biases in MLAGs, to highlight how improper algorithm training leads to ...