We live in a world where activities and interactions are recorded as data: food consumption, workout activities, buying and selling products, sharing information and experiences, borrowing and lending money, and exchanging excess resources. Scientists use the rich data of these activities to understand human social behavior, generate accurate predictions, and make policy recommendations. Machine learning traditionally take such data as given, often treating them as independent samples from some unknown statistical distribution. However, such data are possessed or generated by potentially strategic people in the context of specific interaction rules. Hence, what data become available depends on the interaction rules. For example, people with sensitive medical conditions may not reveal their medical data in a survey but could be willing to share them when compensated; crowd workers may not put in a good-faith effort in completing a task if they know that the requester cannot verify the quality of their contributions. In this talk, I argue that a holistic view that jointly considers data acquisition and learning is important. I will discuss two projects. The first project considers acquiring data from strategic data holders who have private cost for revealing their data and then learning from the acquired data. We provide a risk bound on learning, analogous to classic risk bounds, for situations when agents’ private costs can correlate with their data in arbitrary ways. The second project leverages techniques in learning to design a mechanism for obtaining high-quality data from strategic data holders. The mechanism has a strong incentive property: it is a dominant strategy for each agent to truthfully reveal their data even if we have no ground truth to directly evaluate their contributions.

This talk is based on joint works with Jacob Abernethy, Chien-Ju Ho, Yang Liu, and Bo Waggoner.