How can federated learning, which trains models on decentralized data sources, be used in a way that promotes fairness across different participating groups?

Aug 22, 2024 ... Discover why bias and variance are two key components that you must consider when developing any good, accurate machine learning model. This manuscript is a literature study that provides a detailed survey regarding the different categories of bias and the corresponding approaches. 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 ... Jun 3, 2014 ... We conduct a meta-analysis of all relevant, high quality primary studies of defect prediction to determine what factors influence predictive performance. 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 ... 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 ... 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 ... Mar 10, 2025 ... PhyloFrame is an equitable machine learning method that creates signatures of disease based on population genomics data and disease-relevant ... 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.