How can we develop fairness metrics that are sensitive to different types of bias?

Feb 7, 2024 ... The landscape is overrun with “neutral” analysts using some kind of sentiment analysis to lazily label something as right or left. This MIT ... The biases include those related to missing data and patients not identified by algorithms, sample size and underestimation, and misclassification and ... Aug 13, 2018 ... This paper argues that algorithmic biases explicitly and implicitly harm racial groups and lead to forms of discrimination. Aug 26, 2022 ... The test provides a strict control for type I errors and high statistical power, even for nonnormally and nonlinearly dependent predictions, often seen in ... A method of assessing and correcting inaccuracies in a machine learning model. It involves measuring the difference between the model's predicted outcomes and ... Mar 15, 2022 ... In this article, we will cover bias concerning ML modeling, types of biases involved in developing machine learning models, methods to detect biases, and their ... May 1, 2022 ... It is the difference between the average estimator value (averaged over random samples) and the true value of the parameter. Feb 21, 2022 ... A model's ability to generalize is influenced by both the diversity of the data and the way the model is trained, researchers report. Jul 25, 2022 ... Machine learning bias is sometimes called algorithm bias or AI bias. Predictive models may be trained on data containing human decisions or ... This manuscript is a literature study that provides a detailed survey regarding the different categories of bias and the corresponding approaches.