理论进，理论出：社会理论如何解决机器学习不能解决的问题（Computers and Society）

• 2020 年 1 月 13 日
• 筆記

机器学习和社会科学交叉领域的研究为社会行为提供了重要的新视角。与此同时，学者们已经发现了许多机器学习方法，如果不小心使用，也会导致关于人的错误和有害的主张（例如关于性别的生物学本质），和/或产生歧视性的结果。在这里，我们认为这些问题的出现主要是因为缺乏或误用了社会理论。通过机器学习管道的每一步，我们确定了社会理论必须参与的方式，以解决技术本身无法解决的问题，并提供了一条将理论用于这一目的的途径。

原文标题：Computers and Society：Theory In, Theory Out: How social theory can solve problems that machine learning can't

原文：Research at the intersection of machine learning and the social sciences has provided critical new insights into social behavior. At the same time, scholars have identified myriad ways in which machine learning, when applied without care, can also lead to incorrect and harmful claims about people (e.g. about the biological nature of sexuality), and/or to discriminatory outcomes. Here, we argue that such issues arise primarily because of the lack of, or misuse of, social theory. Walking through every step of the machine learning pipeline, we identify ways in which social theory must be involved in order to address problems that technology alone cannot solve, and provide a pathway towards the use of theory to this end.

原文作者：Jason Radford,Kenneth Joseph 原文链接：https://arxiv.org/abs/2001.03203