Digital Gatekeepers: Exploring Large Language Model's Role in Immigration Decisions
This addresses the challenge of automating and ensuring fairness in immigration decisions for government departments, though it is incremental in exploring AI applications in this domain.
The study investigated the use of large language models like GPT-3.5 and GPT-4 to support immigration decision-making, finding that they can align with human strategies for utility maximization and procedural fairness but still exhibit biases and stereotypes based on nationality and privilege.
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.