How can federated learning be used to train fair models on decentralized data?

We argue that algorithmic bias evolves with human interaction in an iterative manner, which may have a long-term effect on algorithm performance and humans' ... Mar 12, 2023 ... This manuscript is a literature study that provides a detailed survey regarding the different categories of bias and the corresponding approaches. Feb 4, 2019 ... Bias can creep in at many stages of the deep-learning process, and the standard practices in computer science aren't designed to detect it. Feb 21, 2024 ... USC computer science researchers propose a novel approach to mitigate bias in machine learning model training, specifically in image generation. Feb 8, 2024 ... We developed a unified transformer-based machine learning framework ImmuBPI that comprised three tasks and achieved state-of-the-art performance. In linear regression analysis, bias refers to the error that is introduced by approximating a real-life problem, which may be complicated, by a much simpler ... Oct 16, 2023 ... AI bias, also referred to as machine learning bias or algorithm bias, refers to AI systems that produce biased results that reflect and ... Previous research revealed the existence of large near-surface temperature biases (mostly warm) over the Arctic sea ice in the current generation of atmospheric ... Feb 7, 2024 ... The landscape is overrun with “neutral” analysts using some kind of sentiment analysis to lazily label something as right or left. This MIT ...