Towards Objective and Unbiased Decision Assessments with LLM-Enhanced Hierarchical Attention Networks

• URL: http://arxiv.org/abs/2411.08504v2
• Date: Thu, 14 Nov 2024 05:51:26 GMT
• Title: Towards Objective and Unbiased Decision Assessments with LLM-Enhanced Hierarchical Attention Networks
• Authors: Junhua Liu, Kwan Hui Lim, Roy Ka-Wei Lee,
• Abstract summary: This work investigates cognitive bias identification in high-stake decision making process by human experts. We propose bias-aware AI-augmented workflow that surpass human judgment. In our experiments, both the proposed model and the agentic workflow significantly improves on both human judgment and alternative models.
• Score: 6.520709313101523
• License:
• Abstract: 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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