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.

翻译：当机器学习任务被外包给理性主体时，利益冲突可能产生并严重影响预测性能。本文提出了一种面向激励感知的机器学习任务委托理论框架。我们将委托建模为委托-代理博弈，其中委托人可通过基于绩效的合同激励精准学习。通过将契约设计的经济学理论适配至该场景，我们定义了预算最优合同，并证明在合理假设下这类合同具有简单的阈值形式。在二元行动情况下，此类合同的最优性可等价于经典的奈曼-皮尔逊引理，从而建立了契约设计与统计假设检验之间的形式化联系。实验表明，利用学习曲线与尺度律研究的最新进展，可通过小规模数据构建预算最优合同。我们采用合成数据与现实世界分类任务评估了其性能与经济效果。