{"ID":2859446,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.06093","arxiv_id":"2510.06093","title":"Classical AI vs. LLMs for Decision-Maker Alignment in Health Insurance Choices","abstract":"As algorithmic decision-makers are increasingly applied to high-stakes domains, AI alignment research has evolved from a focus on universal value alignment to context-specific approaches that account for decision-maker attributes. Prior work on Decision-Maker Alignment (DMA) has explored two primary strategies: (1) classical AI methods integrating case-based reasoning, Bayesian reasoning, and naturalistic decision-making, and (2) large language model (LLM)-based methods leveraging prompt engineering. While both approaches have shown promise in limited domains such as medical triage, their generalizability to novel contexts remains underexplored. In this work, we implement a prior classical AI model and develop an LLM-based algorithmic decision-maker evaluated using a large reasoning model (GPT-5) and a non-reasoning model (GPT-4) with weighted self-consistency under a zero-shot prompting framework, as proposed in recent literature. We evaluate both approaches on a health insurance decision-making dataset annotated for three target decision-makers with varying levels of risk tolerance (0.0, 0.5, 1.0). In the experiments reported herein, classical AI and LLM-based models achieved comparable alignment with attribute-based targets, with classical AI exhibiting slightly better alignment for a moderate risk profile. The dataset and open-source implementation are publicly available at: https://github.com/TeX-Base/ClassicalAIvsLLMsforDMAlignment and https://github.com/Parallax-Advanced-Research/ITM/tree/feature_insurance.","short_abstract":"As algorithmic decision-makers are increasingly applied to high-stakes domains, AI alignment research has evolved from a focus on universal value alignment to context-specific approaches that account for decision-maker attributes. Prior work on Decision-Maker Alignment (DMA) has explored two primary strategies: (1) cla...","url_abs":"https://arxiv.org/abs/2510.06093","url_pdf":"https://arxiv.org/pdf/2510.06093v1","authors":"[\"Mallika Mainali\",\"Harsha Sureshbabu\",\"Anik Sen\",\"Christopher B. Rauch\",\"Noah D. Reifsnyder\",\"John Meyer\",\"J. T. Turner\",\"Michael W. Floyd\",\"Matthew Molineaux\",\"Rosina O. Weber\"]","published":"2025-10-07T16:21:52Z","proceeding":"cs.AI","tasks":"[\"cs.AI\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false,"code_links":[{"ID":608641,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2859446,"paper_url":"https://arxiv.org/abs/2510.06093","paper_title":"Classical AI vs. LLMs for Decision-Maker Alignment in Health Insurance Choices","repo_url":"https://github.com/TeX-Base/ClassicalAIvsLLMsforDMAlignment","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0},{"ID":608642,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2859446,"paper_url":"https://arxiv.org/abs/2510.06093","paper_title":"Classical AI vs. LLMs for Decision-Maker Alignment in Health Insurance Choices","repo_url":"https://github.com/Parallax-Advanced-Research/ITM","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}