Delegated Classification

• URL: http://arxiv.org/abs/2306.11475v2
• Date: Tue, 5 Dec 2023 21:11:21 GMT
• Title: Delegated Classification
• Authors: Eden Saig, Inbal Talgam-Cohen, Nir Rosenfeld
• Abstract summary: We propose a theoretical framework for incentive-aware delegation of machine learning tasks. We define budget-optimal contracts and prove they take a simple threshold form under reasonable assumptions. Empirically, we demonstrate that budget-optimal contracts can be constructed using small-scale data.
• Score: 21.384062337682185
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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