How can we avoid creating feedback loops where AI is used to reinforce or amplify existing biases?

Feb 7, 2022 ... We present a guide to the data ecosystem used by health insurers to highlight where bias can arise along machine learning pipelines. We suggest ... Tackling issues of bias and fairness when building and deploying machine learning and data science systems has received increased attention from the research ... Sep 15, 2023 ... In this article, we will embark on a journey to understand bias and fairness in machine learning algorithms, exploring their implications, real-world examples, ... 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 ... Both of these existing methods address the problem of bias by adjusting the machine learning model. However, oftentimes it is the training data itself – i.e., ... Jan 31, 2018 ... This post assumes some familiarity with machine learning, and reinforcement learning in particular. If you are new to RL, I'd recommend ... Aug 13, 2018 ... This paper argues that algorithmic biases explicitly and implicitly harm racial groups and lead to forms of discrimination. Mar 22, 2024 ... Research suggests that digital ageism, that is, age-related bias, is present in the development and deployment of machine learning (ML) models. 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. Defining data bias and the list of common types of data bias in ML: selection bias, overfitting/underfitting, outliers, measurement bias, recall bias, ...