How can we involve affected communities in the development of fairer AI?

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 29, 2022 ... One of the main areas for concern is bias in AI systems. Bias can inappropriately skew the output from AI in favor of certain data sets; ... Aug 18, 2023 ... Here, we apply machine learning (ML) to produce a blended TROPOMI+GOSAT product that uses GOSAT to correct biases in the TROPOMI data and ... Mar 15, 2022 ... This document uses terms such as AI technology, AI system, and AI applications inter- changeably. Terms related to the machine learning pipeline ... The biases include those related to missing data and patients not identified by algorithms, sample size and underestimation, and misclassification and ... 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. 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 ... 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 ... 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 ... Feb 21, 2022 ... A model's ability to generalize is influenced by both the diversity of the data and the way the model is trained, researchers report.