摘要
一位新闻记者-机器人与机器学习的工作人员新闻编辑每日新闻-机器学习的新研究是一篇报告的主题。根据NewsRx编辑在英国牛津的新闻报道，研究表明：“高收入国家(HICs)和中等收入国家(LMICs)之间，人工智能(AI)的合作努力越来越普遍。鉴于低收入国家往往面临资源限制，合作对于汇集资源、专业知识和知识至关重要。”这项研究的财政支持者包括地平线2020框架计划、惠康信托基金、国家卫生和护理研究所。我们的新闻记者从O Xford大学的研究中获得了一句话，“尽管有明显的优势，但确保SE合作模式的公平性和公平性是至关重要的，特别是考虑到LMIC和HIC医院之间的明显差异。在这项研究中，我们表明，协作人工智能方法可以导致HIC和LMIC设置的不同绩效结果。”特别是在存在数据不平衡的情况下。通过一个真实的COV ID-19筛查案例研究，我们证明实施算法级B IAS缓解方法显著提高了HIC和L MIC站点之间的结果公平性，同时保持了高诊断敏感性。
Abstract
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."
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