首页|Recent Findings from Baylor University Has Provided New Informa- tion about Machine Learning (Fairness Issues, Current Approaches, and Challenges In Machine Learning Models)
Recent Findings from Baylor University Has Provided New Informa- tion about Machine Learning (Fairness Issues, Current Approaches, and Challenges In Machine Learning Models)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
Springer Nature
Investigators discuss new findings in Machine Learning. According to news reporting out of Waco, Texas, by NewsRx editors, research stated, "With the increasing influence of machine learning algorithms in decision-making processes, concerns about fairness have gained significant attention. This area now offers significant literature that is complex and hard to penetrate for newcomers to the domain." Financial support for this research came from National Foundation for Science and Technology Devel- opment. Our news journalists obtained a quote from the research from Baylor University, "Thus, a mapping study of articles exploring fairness issues is a valuable tool to provide a general introduction to this field. Our paper presents a systematic approach for exploring existing literature by aligning their discoveries with predetermined inquiries and a comprehensive overview of diverse bias dimensions, encompassing training data bias, model bias, conflicting fairness concepts, and the absence of prediction transparency, as ob- served across several influential articles. To establish connections between fairness issues and various issue mitigation approaches, we propose a taxonomy of machine learning fairness issues and map the diverse range of approaches scholars developed to address issues. We briefly explain the responsible critical factors behind these issues in a graphical view with a discussion and also highlight the limitations of each approach analyzed in the reviewed articles."
WacoTexasUnited StatesNorth and Central AmericaCyborgsEmerging TechnologiesMachine LearningBaylor University
NETL
NSTL
Springer Nature
