How can practitioners choose the most appropriate fairness metric or set of metrics for a specific real-world application?

Tackling issues of bias and fairness when building and deploying machine learning and data science systems has received increased attention from the research ... May 22, 2019 ... The private and public sectors are increasingly turning to artificial intelligence (AI) systems and machine learning algorithms to automate ... May 31, 2022 ... Additionally, ML algorithms tend to overfit to the data on which they are trained, which entails the learning of spurious relationships present ... 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 ... Jan 21, 2021 ... Identifying Systemic Bias in the Acquisition of Machine Learning Decision Aids for Law Enforcement Applications ... Biased software tools that use ... Aug 13, 2018 ... This paper argues that algorithmic biases explicitly and implicitly harm racial groups and lead to forms of discrimination. Mar 12, 2023 ... 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. Jul 19, 2021 ... ... bias in artificial intelligence was identified and the ethical risk mitigated. ... We can build a machine-learning model that works well at ... 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 ...