How objective and unbiased are we while making decisions? This work investigates cognitive bias identification in high-stake decision making process by human experts, questioning its effectiveness in real-world settings, such as candidates assessments for university admission. We begin with a statistical analysis assessing correlations among different decision points among in the current process, which discovers discrepancies that imply cognitive bias and inconsistency in decisions. This motivates our exploration of bias-aware AI-augmented workflow that surpass human judgment. We propose BGM-HAN, a hierarchical attention network enhanced by byte-pair encoding, multi-head attention and gated residual connection. Using it as backbone model, we further propose a Shortlist-Analyse-Recommend (SAR) agentic workflow, which simulate real-world decision-making. In our experiments, both the proposed model and the agentic workflow significantly improves on both human judgment and alternative models, validated with real-world data.

翻译：我们在决策过程中究竟有多客观和公正？本研究针对人类专家在高风险决策过程中的认知偏差识别展开探讨，质疑其在现实场景（如大学入学候选人评估）中的有效性。我们首先通过统计分析评估当前流程中不同决策点之间的相关性，发现存在表明认知偏差与决策不一致性的差异。这促使我们探索能够超越人类判断的、具备偏差感知能力的人工智能增强工作流。我们提出BGM-HAN模型——一种通过字节对编码、多头注意力机制和门控残差连接增强的层次化注意力网络。以此作为骨干模型，我们进一步提出模拟现实决策过程的"初筛-分析-推荐"智能体工作流。实验结果表明，基于真实数据的验证，所提出的模型与智能体工作流在人类判断和替代模型比较中均取得显著提升。