Towards Objective and Unbiased Decision Assessments with LLM-Enhanced Hierarchical Attention Networks
This addresses bias in expert decision-making for domains like admissions, though it appears incremental as it builds on existing hierarchical attention methods.
The paper tackles cognitive bias in high-stake decisions like university admissions by proposing BGM-HAN, an enhanced Hierarchical Attention Network, and a SAR agentic workflow, which significantly improve over human judgment and alternative models in experiments with real-world data.
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.