What are some best practices for building fair and unbiased AI systems?

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. Feb 10, 2021 ... The application of machine learning (ML) technologies in medicine generally but also in radiology more specifically is hoped to improve ... Feb 21, 2024 ... USC computer science researchers propose a novel approach to mitigate bias in machine learning model training, specifically in image generation. Aug 14, 2018 ... This article is based on Rachel Thomas's keynote presentation, “Analyzing & Preventing Unconscious Bias in Machine Learning” at QCon.ai 2018. As humanities scholars start to make use of the possibilities provided by AI, there is a risk that humanities scholarship can become polluted by hidden gender ... Jul 24, 2024 ... Introduction: Machine learning (ML) algorithms have been heralded as promising solutions to the realization of assistive systems in digital ... Oct 18, 2022 ... We develop and evaluate a machine learning system for identifying five major categories of biased and discriminatory language in job advertisements. 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 ... 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. 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 ...