用AI识别并纠正高校招生中的认知偏见，提升决策客观性。
Towards Objective and Unbiased Decision Assessments with LLM-Enhanced Hierarchical Attention Networks
- 构建分层注意力网络，融合编码与门控残差机制增强表征能力。
- 在真实招生数据上，模型与工作流显著优于人工判断和现有模型。
- 适合需要公平决策的场景，如人才选拔、医疗评估等。
当前高校招生中的人工决策是否存在认知偏见？本研究通过统计分析发现不同决策环节间存在显著关联差异，暗示决策不一致与偏见。为此，我们提出BGM-HAN模型——一种基于字节对编码（Byte-Pair Encoding）、门控残差连接与多头注意力的分层注意力网络。在此基础上，设计了短名单-分析-推荐（SAR）智能体工作流，模拟真实决策过程。实验基于真实招生数据验证：该模型与工作流在客观性与一致性上均显著优于人类判断及现有模型，展现出在高风险决策中超越人类判断的潜力。
原文摘要 · Abstract (English)
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, an enhanced Hierarchical Attention Network with Byte-Pair Encoding, Gated Residual Connections and Multi-Head Attention. Using it as a 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.
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