Human–Artificial Intelligence Collaborative Decision-Making and Error Control in Mobile Accounting Systems

Abstract

Human–artificial intelligence (AI) collaborative decision-making in mobile accounting systems is constrained by static division-of-labor boundaries and AI confidence bias, making automation efficiency and error control difficult to balance. A dynamic confidence threshold framework was proposed in this study. Confidence was calibrated through Platt scaling, reducing the expected calibration error to 0.031. An adaptive threshold function was constructed by integrating task complexity and historical error rates, and manual review task allocation was optimized by incorporating operator experience. Tasks were dynamically routed into three channels: automatic approval, human–AI collaborative correction, and in-depth manual review. Selective-risk statistical monitoring and drift detection mechanisms were also incorporated. Experimental results on a real-world accounting dataset and a mobile prototype system indicate that the auto-pass error rate was controlled at 4.2%, the automation rate reached 78.6%—an improvement of 13.3 percentage points over the optimal fixed threshold— accountant processing time was reduced by 23.0%, and cognitive load was reduced by 26.8%; favorable steady-state recovery was further exhibited under multiple perturbations. A systematic technical solution is thus provided for human–AI collaborative decision-making in mobile accounting scenarios.