中文摘要
尽管假设检验是一项高度形式化的活动，假设生成在很大程度上仍是非形式化的。我们提出了一种生成关于人类行为的新假设的系统程序，该程序利用机器学习算法识别人们可能无法察觉的模式的能力。我们通过一个具体应用来说明该程序：法官关于将谁羁押的决策。我们从一个引人注目的事实出发：仅被告人的面部就对法官的羁押决策产生很大影响。事实上，一个仅给定被告人入狱照片像素的算法，就能解释高达一半的可预测变异。我们开发了一种程序，允许人类被试与该黑箱算法进行交互，从而产生关于面部中何种因素影响法官决策的假设。该程序生成的假设既可解释又新颖：它们不能由人口统计学特征（例如，种族）或现有的心理学研究解释，也不是人们或专家已经知道的（即使是隐含地知道）。尽管这些结果是具体的，但我们的程序是通用的。它提供了一种从任何高维数据集（例如，手机、卫星、在线行为、新闻标题、公司文件和高频时间序列）中生成新颖且可解释的假设的方法。我们文章的一个核心信条是，假设生成是一项有价值的活动，我们希望这能鼓励在科学这一很大程度上处于“前科学”阶段的未来工作。
Abstract
Abstract While hypothesis testing is a highly formalized activity, hypothesis generation remains largely informal. We propose a systematic procedure to generate novel hypotheses about human behavior, which uses the capacity of machine learning algorithms to notice patterns people might not. We illustrate the procedure with a concrete application: judge decisions about whom to jail. We begin with a striking fact: the defendant’s face alone matters greatly for the judge’s jailing decision. In fact, an algorithm given only the pixels in the defendant’s mug shot accounts for up to half of the predictable variation. We develop a procedure that allows human subjects to interact with this black-box algorithm to produce hypotheses about what in the face influences judge decisions. The procedure generates hypotheses that are both interpretable and novel: they are not explained by demographics (e.g., race) or existing psychology research, nor are they already known (even if tacitly) to people or experts. Though these results are specific, our procedure is general. It provides a way to produce novel, interpretable hypotheses from any high-dimensional data set (e.g., cell phones, satellites, online behavior, news headlines, corporate filings, and high-frequency time series). A central tenet of our article is that hypothesis generation is a valuable activity, and we hope this encourages future work in this largely “prescientific” stage of science.