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 phenomenon in a budget-constrained principal-agent framework with strategic human agents whose output accuracy depends on unobserved effort. Our first contribution is a general impossibility result showing that incentive collapse is not merely a limitation of simple linear payments, but arises for any payment rule based only on observed task accuracy.To overcome this barrier, 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.

翻译：AI辅助任务委派日益普遍，但在此类系统中人类付出成本高昂且通常不可观测。Bastani与Cachon（2025）、Sambasivan等（2021）的最新研究表明，基于准确率的支付方案存在激励崩溃现象：随着AI准确率提升，维持正向人类付出需要无限支付。本研究在预算约束的主-代理框架下，分析了策略性人类代理人的产出准确率依赖于不可观测努力的情形。我们的首要贡献是提出一个通用不可能性定理，证明激励崩溃不仅源于简单线性支付的局限，更是任何仅基于观测任务准确率的支付规则所固有的问题。为突破这一障碍，我们提出哨兵审计支付机制，该机制能以有限成本强制实现严格正且可控的人类付出水平，且与AI准确率无关。在此激励稳健基础上，我们构建了激励感知的主动统计推断框架，联合优化（i）审计率与（ii）不同难度任务的主动采样及预算分配，以在单一预算约束下最小化最终统计损失。实验证明，相较于标准主动学习与纯审计基线方法，本方案实现了更优的成本-误差权衡。