What are the different types of fairness audits and their respective strengths and weaknesses?

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. Aug 8, 2023 ... Encord Active offers features to help reduce bias in datasets, allowing you to identify and address any potential biases in your data workflow. Oct 9, 2024 ... Selection bias occurs if a dataset's examples are chosen in a way that is not reflective of their real-world distribution. Selection bias can ... Dec 11, 2018 ... As machine learning systems are used more and more to make decisions about insurance, criminal justice, credit, and child welfare, ... Aug 13, 2018 ... This paper argues that algorithmic biases explicitly and implicitly harm racial groups and lead to forms of discrimination. 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 ... Aug 14, 2018 ... This article is based on Rachel Thomas's keynote presentation, “Analyzing & Preventing Unconscious Bias in Machine Learning” at QCon.ai 2018. Mar 17, 2022 ... In this article, we focused on machine learning bias, answering some of the pivotal questions around the topic, from types to measurement and prevention ... Oct 25, 2019 ... Machine learning systems disregard variables that do not accurately predict outcomes (in the data available to them). This is in contrast to ... Oct 8, 2021 ... Supervised machine learning depends on labelled datasets, often processed and prepared by a data scientist or specialist. Bias in this labelling ...