Organizations increasingly rely on autonomous AI for operational decisions, such as inventory replenishment in smart vending. But when humans are given full power to override the algorithm, performance can suffer—because human bias creeps in. That is the problem addressed in a new arXiv preprint (arXiv:2607.00420).
The paper's abstract reports that granting full override discretion degrades performance, and that a simple solution to how supervision is structured can improve results. The evidence is drawn from smart vending, a setting where AI recommendations and human adjustments directly affect outcomes.
Because only the abstract is available, the exact nature of the proposed solution is not detailed here. Still, the finding points to a broader lesson: the design of human oversight matters as much as the algorithm itself. Restricting override to specific, structured choices may outperform an open-ended veto.