用大模型辅助移民决策，发现其既懂公平又藏偏见。
Digital Gatekeepers: Exploring Large Language Model's Role in Immigration Decisions
- 通过实验和访谈，分析大模型如何做移民决策
- 模型倾向追求效率与程序公正，但仍存国籍偏见
- 适合关注AI公平性与政策应用的研究者阅读
随着全球化和移民人口增加，移民部门面临巨大工作压力，亟需确保决策公平。本文研究大语言模型（如GPT-3.5和GPT-4）在支持移民决策中的潜力。采用混合方法，结合离散选择实验与深度访谈，探究大模型的决策策略及其公平性。结果表明，大模型能模仿人类以效用最大化和程序公平为导向进行决策；然而，尽管ChatGPT设有防歧视机制，仍存在对国籍的刻板印象，表现出对特权群体的偏好。该双重分析揭示了大模型在自动化移民决策中既具潜力也存在局限。
原文摘要 · Abstract (English)
With globalization and increasing immigrant populations, immigration departments face significant work-loads and the challenge of ensuring fairness in decision-making processes. Integrating artificial intelligence offers a promising solution to these challenges. This study investigates the potential of large language models (LLMs),such as GPT-3.5 and GPT-4, in supporting immigration decision-making. Utilizing a mixed-methods approach,this paper conducted discrete choice experiments and in-depth interviews to study LLM decision-making strategies and whether they are fair. Our findings demonstrate that LLMs can align their decision-making with human strategies, emphasizing utility maximization and procedural fairness. Meanwhile, this paper also reveals that while ChatGPT has safeguards to prevent unintentional discrimination, it still exhibits stereotypes and biases concerning nationality and shows preferences toward privileged group. This dual analysis highlights both the potential and limitations of LLMs in automating and enhancing immigration decisions.
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