  When machine learning is outsourced to a rational agent, conflicts of
interest might arise and severely impact predictive performance. In this work,
we propose a theoretical framework for incentive-aware delegation of machine
learning tasks. We model delegation as a principal-agent game, in which
accurate learning can be incentivized by the principal using performance-based
contracts. Adapting the economic theory of contract design to this setting, we
define budget-optimal contracts and prove they take a simple threshold form
under reasonable assumptions. In the binary-action case, the optimality of such
contracts is shown to be equivalent to the classic Neyman-Pearson lemma,
establishing a formal connection between contract design and statistical
hypothesis testing. Empirically, we demonstrate that budget-optimal contracts
can be constructed using small-scale data, leveraging recent advances in the
study of learning curves and scaling laws. Performance and economic outcomes
are evaluated using synthetic and real-world classification tasks.
