What are the challenges in defining and measuring fairness in real-world applications?

Mar 26, 2018 ... In the case of self-learning systems, the type of “garbage” is biased data. Left unchecked, feeding biased data to self-learning systems can ... Previous research revealed the existence of large near-surface temperature biases (mostly warm) over the Arctic sea ice in the current generation of atmospheric ... Apr 3, 2025 ... Bias Variance Tradeoff. If the algorithm is too simple (hypothesis with linear equation) then it may be on high bias and low variance condition ... Jul 12, 2023 ... Healthcare algorithms and AI bias can contribute to existing health disparities for certain populations based on race, ethnicity, gender, age, or other ... Dec 28, 2018 ... In this paper, we reconcile the classical understanding and the modern practice within a unified performance curve. This "double descent" curve ... 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. 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 ... Oct 8, 2021 ... Supervised machine learning depends on labelled datasets, often processed and prepared by a data scientist or specialist. Bias in this labelling ... 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 ... Detecting bias in your ML model can be done proactively or reactively, and depending on your specific case, either one is viable. Reactive bias detection ...