Research at the intersection of machine learning and the social sciences has provided critical new insights into social behavior. At the same time, a variety of critiques have been raised ranging from technical issues with the data used and features constructed, problematic assumptions built into models, their limited interpretability, and their contribution to bias and inequality. We argue such issues arise primarily because of the lack of social theory at various stages of the model building and analysis. In the first half of this paper, we walk through how social theory can be used to answer the basic methodological and interpretive questions that arise at each stage of the machine learning pipeline. In the second half, we show how theory can be used to assess and compare the quality of different social learning models, including interpreting, generalizing, and assessing the fairness of models. We believe this paper can act as a guide for computer and social scientists alike to navigate the substantive questions involved in applying the tools of machine learning to social data.

中文翻译：

---

Theory In, Theory Out：社会理论在机器学习中的社会科学应用

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见解。与此同时，人们提出了各种各样的批评，包括所用数据和构建特征的技术问题、模型中内置的有问题的假设、其有限的可解释性以及它们对偏见和不平等的贡献。我们认为这些问题的出现主要是因为在模型构建和分析的各个阶段缺乏社会理论。在本文的前半部分，我们将介绍如何使用社会理论来回答机器学习流程每个阶段出现的基本方法论和解释性问题。在后半部分，我们展示了如何使用理论来评估和比较不同社会学习模型的质量，包括口译、概括和评估模型的公平性。我们相信这篇论文可以作为计算机和社会科学家的指南，以解决将机器学习工具应用于社会数据所涉及的实质性问题。