理論進，理論出：社會理論如何解決機器學習不能解決的問題（Computers and Society）

• 2020 年 1 月 13 日
• 筆記

機器學習和社會科學交叉領域的研究為社會行為提供了重要的新視角。與此同時，學者們已經發現了許多機器學習方法，如果不小心使用，也會導致關於人的錯誤和有害的主張（例如關於性別的生物學本質），和/或產生歧視性的結果。在這裡，我們認為這些問題的出現主要是因為缺乏或誤用了社會理論。通過機器學習管道的每一步，我們確定了社會理論必須參與的方式，以解決技術本身無法解決的問題，並提供了一條將理論用於這一目的的途徑。

原文標題：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 原文鏈接：https://arxiv.org/abs/2001.03203