Leveraging Human Features at Test-Time

Machine learning (ML) models can make decisions based on large amounts of data, but they may be missing important context. For example, a model trained to predict psychiatric outcomes may know nothing about a patient's social support system, and social support may look different for different patients. In this work, we explore strategies for querying for a small, additional set of these human features that are relevant for each specific instance at test time, so as to incorporate this information while minimizing the burden to the user to label feature values. We define the problem of querying users for an instance-specific set of human feature values, and propose algorithms to solve it. We show in experiments on real datasets that our approach outperforms a feature selection baseline that chooses the same set of human features for all instances.