机器学习与社会研究的内部人士和外部人士
Insiders and Outsiders in Research on Machine Learning and Society
摘要：
机器学习研究的一个子集与社会问题交叉，包括公平、问责制和透明度，以及将机器学习用于社会公益。在这项工作中，我们通过科学社会学的视角，分析了在机器学习和社会的交叉点上为这项研究做出贡献的学者。通过分析所有发表在arXiv上的机器学习论文的作者，我们发现，与来自代表性过高背景（由性别和种族/民族定义）的研究人员相比，来自代表性不足背景的研究人员更有可能在这个交叉点进行研究，而不是其他类型的机器学习研究。这种状况导致了两种关于科学企业内部人和外部人的观点之间的争论：外部人是指被研究群体之外的人，而外部人是指那些历史上没有作为研究人员参与某个领域的人。这种争论表现为一个认识问题，即从机器学习研究中的生活经验中获得的知识的有效性，并预测了我们在现实世界中看到的边界工作。
英文摘要：
A subset of machine learning research intersects with societal issues, including fairness, accountability and transparency, as well as the use of machine learning for social good. In this work, we analyze the scholars contributing to this research at the intersection of machine learning and society through the lens of the sociology of science. By analyzing the authorship of all machine learning papers posted to arXiv, we show that compared to researchers from overrepresented backgrounds (defined by gender and race/ethnicity), researchers from underrepresented backgrounds are more likely to conduct research at this intersection than other kinds of machine learning research. This state of affairs leads to contention between two perspectives on insiders and outsiders in the scientific enterprise: outsiders being those outside the group being studied, and outsiders being those who have not participated as researchers in an area historically. This contention manifests as an epistemic question on the validity of knowledge derived from lived experience in machine learning research, and predicts boundary work that we see in a real-world example.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
