Overcoming the Incentive Collapse Paradox

2026-03-27

AI Summary

This study addresses the collapse of human effort incentives in AI-assisted task delegation under conventional accuracy-based payment schemes, which require unbounded compensation to sustain effective human engagement as AI performance improves. To resolve this issue within a budget-constrained principal–agent framework, the authors propose a sentinel auditing payment mechanism that decouples rewards from AI accuracy, thereby stably incentivizing positive human effort at finite cost. Furthermore, they introduce the first joint optimization of incentive mechanisms and active learning, establishing an incentive-aware active statistical inference framework that co-optimizes audit frequency, task-difficulty-adaptive sampling strategies, and budget allocation. Experiments demonstrate that the proposed approach significantly outperforms both standard active learning and audit-only baselines in the trade-off between cost and estimation error.

Abstract

AI-assisted task delegation is increasingly common, yet human effort in such systems is costly and typically unobserved. Recent work by Bastani and Cachon (2025); Sambasivan et al. (2021) shows that accuracy-based payment schemes suffer from incentive collapse: as AI accuracy improves, sustaining positive human effort requires unbounded payments. We study this problem in a budget-constrained principal-agent framework with strategic human agents whose output accuracy depends on unobserved effort. We propose a sentinel-auditing payment mechanism that enforces a strictly positive and controllable level of human effort at finite cost, independent of AI accuracy. Building on this incentive-robust foundation, we develop an incentive-aware active statistical inference framework that jointly optimizes (i) the auditing rate and (ii) active sampling and budget allocation across tasks of varying difficulty to minimize the final statistical loss under a single budget. Experiments demonstrate improved cost-error tradeoffs relative to standard active learning and auditing-only baselines.

Problem

• incentive collapse
• AI-assisted delegation
• unobserved effort
• budget constraint
• principal-agent

Innovation

• sentinel-auditing
• incentive collapse
• principal-agent
• active learning
• budget-constrained inference