Debiasing accounting judgment and decision-making

Cognitive biases undermine the accuracy and reliability of accounting tasks. Yet the literature on debiasing techniques remains fragmented, leaving challenges unresolved. Drawing on a comprehensive review of seventy-five accounting studies, this article introduces a Debiasing Wheel. The framework first identifies eighteen accounting tasks and the twenty cognitive biases that affect them, then maps sixteen debiasing techniques into five intervention types – cognitive fluency, choice architecture, conscientious engagement, cognitive reframing, and outside view – aligned with the three phases of judgment and decision-making (options identification, options evaluation, and choice). Rather than offering a universal taxonomy of biases or a one-size-fits-all solution, the Debiasing Wheel delineates the scope conditions under which specific techniques are expected to be effective, conditional on task characteristics and the judgment and decision-making phase. We identify directions for future research, including targeting under-addressed biases, examining the limits of debiasing and alternative approaches, integrating heuristics as tools for bias mitigation, and exploring AI-driven techniques to enhance judgment and decision-making processes. In short, the Debiasing Wheel defines what to fix (task-specific biases), how to fix it (techniques within intervention types), and when to act (AJDM timing), providing a unified guide for improving AJDM.