Adaptive Contracts for Cost-Effective AI Delegation

2026-03-17

AI Summary

This work addresses the challenge of performance-based AI outsourcing, where noisy evaluations inflate expected payments, while fine-grained assessments—though effective at reducing noise—are prohibitively costly. To balance incentive efficiency and evaluation cost, we introduce adaptive evaluation into AI delegation contract design for the first time, proposing a two-stage adaptive contract mechanism: an initial low-cost coarse evaluation is followed by a selectively triggered fine-grained assessment. We develop efficient algorithms to compute optimal contracts in specific settings and formulate a randomized adaptive model. Integrating mechanism design, approximation algorithms, and complexity analysis, our experiments on question-answering and code generation tasks demonstrate that this approach significantly outperforms non-adaptive baselines across multiple datasets, effectively curbing excess payments induced by evaluation noise while maintaining cost control.

Technology Category

• Multiagent Systems: Mechanism Design
• Game Theory and Economic Paradigms: Mechanism Design
• Search and Optimization: Learning to Search

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

When organizations delegate text generation tasks to AI providers via pay-for-performance contracts, expected payments rise when evaluation is noisy. As evaluation methods become more elaborate, the economic benefits of decreased noise are often overshadowed by increased evaluation costs. In this work, we introduce adaptive contracts for AI delegation, which allow detailed evaluation to be performed selectively after observing an initial coarse signal in order to conserve resources. We make three sets of contributions: First, we provide efficient algorithms for computing optimal adaptive contracts under natural assumptions or when core problem dimensions are small, and prove hardness of approximation in the general unstructured case. We then formulate alternative models of randomized adaptive contracts and discuss their benefits and limitations. Finally, we empirically demonstrate the benefits of adaptivity over non-adaptive baselines using question-answering and code-generation datasets.

Problem

• AI delegation
• pay-for-performance contracts
• evaluation noise
• evaluation cost
• adaptive contracts

Innovation

• adaptive contracts
• AI delegation
• pay-for-performance
• evaluation cost
• noisy evaluation