Overcoming the Incentive Collapse Paradox
For organizations using AI-assisted human labor, this work provides a practical mechanism to maintain human oversight without unbounded costs, addressing a key bottleneck in delegation systems.
The paper addresses the incentive collapse paradox in AI-assisted task delegation, where accuracy-based payments require unbounded costs as AI improves. It proposes a sentinel-auditing mechanism that ensures positive human effort at finite cost and an active inference framework that jointly optimizes auditing and sampling, achieving better cost-error tradeoffs than baselines.
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.