How do existing societal inequalities get encoded in training datasets?

Dec 11, 2024 ... In the first of a series of research notes on bias in artificial intelligence (AI), this literature review examines available literature on ... Jul 27, 2023 ... Bias is an additional parameter in a neural network that is used to adjust the output along with the weighted sum of the inputs to the neuron. Feb 7, 2022 ... We present a guide to the data ecosystem used by health insurers to highlight where bias can arise along machine learning pipelines. We suggest ... 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 ... 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. Jun 3, 2014 ... We conduct a meta-analysis of all relevant, high quality primary studies of defect prediction to determine what factors influence predictive performance. About the AMBP-L™ Certification System. IDC's Artificial & Machine Bias Prevention - Leader (AMBP-L)™ program enhances enterprise value by bridging the future ... In statistics and machine learning, the bias–variance tradeoff describes the relationship between a model's complexity, the accuracy of its predictions, ... Mar 26, 2018 ... In the case of self-learning systems, the type of “garbage” is biased data. Left unchecked, feeding biased data to self-learning systems can ... 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 ...