How can bias affect the use of machine learning in criminal justice, such as risk assessment tools?

Mar 3, 2021 ... In this paper we make three contributions. First, we assess the compatibility of fairness metrics used in machine learning against the aims and purpose of EU ... 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 ... This paper postulates that the root causes of bias are the prior decisions that affect- (a) what data was selected and (b) the labels assigned to those ... Mar 10, 2025 ... PhyloFrame is an equitable machine learning method that creates signatures of disease based on population genomics data and disease-relevant ... Sep 11, 2024 ... Each chapter addresses a different topic and real-world case studies are featured throughout that highlight discriminatory machine learning ... Sep 1, 2022 ... Bias – A significant imbalance in the input data or model predictions across different facet values. What constitutes “significant” depends on ... 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. The purpose of this study is to illustrate the harmful effects of learned human-like biases in MLAGs, to highlight how improper algorithm training leads to ... Sep 3, 2024 ... AI bias occurs when an AI system produces systematically prejudiced results due to erroneous assumptions in the machine learning process. These ... Apr 27, 2023 ... Bias can be weeded out in a timely fashion if you collect reliable and comprehensive training data, select the most appropriate learning model, conduct ...