Overcoming the Incentive Collapse Paradox

This paper introduces and addresses the incentive collapse paradox, a phenomenon where accuracy-based payments fail to motivate human effort as AI assistance becomes more reliable. The authors demonstrate that if human workers only receive rewards based on their final output accuracy, they will eventually free-ride on the AI’s suggestions rather than exert costly verification effort. To solve this, they propose a sentinel-auditing mechanism that deliberately injects occasional, detectable AI errors to reward human vigilance independently of the AI's natural performance. This strategy is further integrated into an incentive-aware active statistical inference framework, which jointly optimizes budget allocation and task sampling. Theoretical results and experiments on survey and protein data show that this approach maintains high label quality at a finite cost. Ultimately, the research proves that accounting for strategic human behavior allows for more cost-effective and precise statistical estimation than traditional methods.