  Motivated by the emergence of decentralized machine learning (ML) ecosystems,
we study the delegation of data collection. Taking the field of contract theory
as our starting point, we design optimal and near-optimal contracts that deal
with two fundamental information asymmetries that arise in decentralized ML:
uncertainty in the assessment of model quality and uncertainty regarding the
optimal performance of any model. We show that a principal can cope with such
asymmetry via simple linear contracts that achieve 1-1/e fraction of the
optimal utility. To address the lack of a priori knowledge regarding the
optimal performance, we give a convex program that can adaptively and
efficiently compute the optimal contract. We also study linear contracts and
derive the optimal utility in the more complex setting of multiple
interactions.
