首页|University of Oxford Reports Findings in Machine Learning (Mitigating machine le arning bias between high income and low-middle income countries for enhanced mod el fairness and generalizability)
University of Oxford Reports Findings in Machine Learning (Mitigating machine le arning bias between high income and low-middle income countries for enhanced mod el fairness and generalizability)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Machine Learning is th e subject of a report. According to news reporting out of Oxford, United Kingdom , by NewsRx editors, research stated, "Collaborative efforts in artificial intel ligence (AI) are increasingly common between high-income countries (HICs) and lo w- to middle-income countries (LMICs). Given the resource limitations often enco untered by LMICs, collaboration becomes crucial for pooling resources, expertise , and knowledge." Financial supporters for this research include Horizon 2020 Framework Programme, Wellcome Trust, National Institute for Health and Care Research. Our news journalists obtained a quote from the research from the University of O xford, "Despite the apparent advantages, ensuring the fairness and equity of the se collaborative models is essential, especially considering the distinct differ ences between LMIC and HIC hospitals. In this study, we show that collaborative AI approaches can lead to divergent performance outcomes across HIC and LMIC set tings, particularly in the presence of data imbalances. Through a real-world COV ID-19 screening case study, we demonstrate that implementing algorithmic-level b ias mitigation methods significantly improves outcome fairness between HIC and L MIC sites while maintaining high diagnostic sensitivity."
OxfordUnited KingdomEuropeCyborgsDiagnostics and ScreeningEmerging TechnologiesHospitalsMachine Learning
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