What are the emerging trends and challenges in the field of bias and fairness in machine learning?

This manuscript is a literature study that provides a detailed survey regarding the different categories of bias and the corresponding approaches. Jul 3, 2021 ... Weights and biases (commonly referred to as w and b) are the learnable parameters of a some machine learning models, including neural networks. Mar 15, 2022 ... This document uses terms such as AI technology, AI system, and AI applications inter- changeably. Terms related to the machine learning pipeline ... Jul 15, 2024 ... As institutions of higher education turn to AI machine learning and data-driven algorithms to make their work more efficient, a new study ... Aug 8, 2024 ... We show that the ML model can effectively reduce climate bias in large-scale wind, temperature, and humidity while preserving their responses to imposed ... May 22, 2019 ... The private and public sectors are increasingly turning to artificial intelligence (AI) systems and machine learning algorithms to automate ... Sep 3, 2024 ... AI bias occurs when an AI system produces systematically prejudiced results due to erroneous assumptions in the machine learning process. These ... Aug 27, 2019 ... In this article, I'll dig into this question, its impact, and look at ways of eliminating bias from machine learning models. As humanities scholars start to make use of the possibilities provided by AI, there is a risk that humanities scholarship can become polluted by hidden gender ... 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 ...