More and more machine learning is applied to human behavior. Increasingly these algorithms suffer from a hidden-but serious-problem. It arises because they often predict one thing while hoping for another. Take a recommender system: It predicts clicks but hopes to identify preferences. Or take an algorithm that automates a radiologist: It predicts in-the-moment diagnoses while hoping to identify their reflective judgments. Psychology shows us the gaps between the objectives of such prediction tasks and the goals we hope to achieve: People can click mindlessly; experts can get tired and make systematic errors. We argue such situations are ubiquitous and call them "inversion problems": The real goal requires understanding a mental state that is not directly measured in behavioral data but must instead be inverted from the behavior. Identifying and solving these problems require new tools that draw on both behavioral and computational science.

中文翻译：

---

反演问题：为什么算法应该推断心理状态而不仅仅是预测行为。


越来越多的机器学习应用于人类行为。这些算法越来越受到隐藏但严重的问题的困扰。出现这种情况是因为他们经常预测一件事，同时又希望得到另一件事。以推荐系统为例：它预测点击次数，但希望识别偏好。或者采用一种使放射科医生自动化的算法：它可以预测即时诊断，同时希望识别他们的反思性判断。心理学向我们展示了此类预测任务的目标与我们希望实现的目标之间的差距：人们可以无意识地点击；专家可能会感到疲倦并犯系统性错误。我们认为这种情况普遍存在，并将其称为“反转问题”：真正的目标需要理解一种心理状态，这种心理状态不能直接在行为数据中测量，而是必须从行为中反转。识别和解决这些问题需要利用行为科学和计算科学的新工具。