Fair Machine Learning-An Analytical Study Based on CiteSpace

Xiang Luo, Jianfeng Cui, Shuai Ma
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Abstract

With the development of machine learning, fair machine learning has started to receive gradual attention. How to mitigate or eliminate the possible unfair decision results of machine learning has become a popular research topic in this field. At present, the research on fair machine learning is still in its initial stage. In this paper, we analyzed the research and articles related to fair machine learning (January 2011 to December 2022) using CiteSpace visualization software, explored research collaboration networks (authors, institutions, and countries), keyword co-occurrence and clustering networks, and literature co-citation and clustering networks, and analyzed and constructed knowledge graphs. To understand the research foundation, related research progress, the latest research directions, and the research methods receiving attention in the field of fair machine learning through the analysis of the knowledge graph. Relevant key articles are discussed, and future research directions are envisioned.
公平机器学习——基于CiteSpace的分析研究
随着机器学习的发展,公平的机器学习开始逐渐受到关注。如何减轻或消除机器学习可能产生的不公平决策结果已成为该领域的热门研究课题。目前,关于公平机器学习的研究还处于起步阶段。本文利用CiteSpace可视化软件对2011年1月至2022年12月期间与公平机器学习相关的研究和文章进行分析,探索研究协作网络(作者、机构和国家)、关键词共现和聚类网络、文献共被引和聚类网络,并分析和构建知识图谱。通过知识图的分析,了解公平机器学习领域的研究基础、相关研究进展、最新研究方向、受关注的研究方法。对相关重点文章进行了讨论,并展望了未来的研究方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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