How can we build feedback mechanisms to continuously improve the fairness of AI applications?

Jan 21, 2021 ... Identifying Systemic Bias in the Acquisition of Machine Learning Decision Aids for Law Enforcement Applications ... Biased software tools that use ... 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 ... Oct 14, 2020 ... The result of this technique is an RF bias-correction model that, when applied to SNODAS, offers improved daily estimates of SWE which can then ... 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. 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 ... Mar 15, 2022 ... In this article, we will cover bias concerning ML modeling, types of biases involved in developing machine learning models, methods to detect biases, and their ... Aug 13, 2018 ... This paper argues that algorithmic biases explicitly and implicitly harm racial groups and lead to forms of discrimination. 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, 2024 ... In the first of a series of research notes on bias in artificial intelligence (AI), this literature review examines available literature on ... Jun 2, 2022 ... This article will discuss the use of AI and ML in cancer care and provide a framework for mitigating bias to achieve cancer equity.