Towards Objective and Unbiased Decision Assessments with LLM-Enhanced Hierarchical Attention Networks
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866915019115986944 |
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| author | Liu, Junhua Lim, Kwan Hui Lee, Roy Ka-Wei |
| author_facet | Liu, Junhua Lim, Kwan Hui Lee, Roy Ka-Wei |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_08504 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Towards Objective and Unbiased Decision Assessments with LLM-Enhanced Hierarchical Attention Networks Liu, Junhua Lim, Kwan Hui Lee, Roy Ka-Wei Computation and Language Artificial Intelligence 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. |
| title | Towards Objective and Unbiased Decision Assessments with LLM-Enhanced Hierarchical Attention Networks |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2411.08504 |