How can explainable AI (XAI) techniques help in identifying and understanding bias?

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 ... Defining data bias and the list of common types of data bias in ML: selection bias, overfitting/underfitting, outliers, measurement bias, recall bias, ... Dec 28, 2018 ... In this paper, we reconcile the classical understanding and the modern practice within a unified performance curve. This "double descent" curve ... Aug 13, 2018 ... This paper argues that algorithmic biases explicitly and implicitly harm racial groups and lead to forms of discrimination. Feb 21, 2024 ... USC computer science researchers propose a novel approach to mitigate bias in machine learning model training, specifically in image generation. Mar 17, 2022 ... In this article, we focused on machine learning bias, answering some of the pivotal questions around the topic, from types to measurement and prevention ... Jun 2, 2022 ... This article will discuss the use of AI and ML in cancer care and provide a framework for mitigating bias to achieve cancer equity. Mar 9, 2023 ... We know that synthetic data remains one of the most reliable and cost-effective solutions out there that improves the performance of the overall solution. May 23, 2016 ... Machine Bias. There's ... The advantage of using a generic statistical learning algorithm is that you KNOW what information goes in. 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.