What is the role of data documentation and transparency in addressing bias?

Sep 5, 2022 ... This article aims to examine some of the ways in which racial bias gets included in AI and touch upon what can be done to mitigate these biases. Dec 11, 2018 ... As machine learning systems are used more and more to make decisions about insurance, criminal justice, credit, and child welfare, ... Model bias, in Machine Learning, refers to the presence of systematic errors in a model that can cause it to consistently make incorrect predictions. Nov 10, 2017 ... Machine learning can perpetuate and even amplify behavioral biases. By design, a social-media site filtering news based on user preferences ... Mar 5, 2019 ... Reports of successful applications of machine learning (ML) methods in structure-based virtual screening (SBVS) are increasing. About the AMBP-L™ Certification System. IDC's Artificial & Machine Bias Prevention - Leader (AMBP-L)™ program enhances enterprise value by bridging the future ... 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 19, 2010 ... A bias value allows you to shift the activation function to the left or right, which may be critical for successful learning. As promising as machine-learning technology is, it can also be susceptible to unintended biases that require careful planning to avoid. Aug 22, 2024 ... Discover why bias and variance are two key components that you must consider when developing any good, accurate machine learning model.