The Generation Gap: Exploring Age Bias in the Value Systems of Large Language Models
This work addresses age bias in LLMs, which is an incremental but important issue for ensuring fairness in AI applications affecting diverse user groups.
The study investigated age bias in Large Language Models (LLMs) by comparing their values to those of different age groups using World Value Survey data, finding a general inclination towards younger demographics with variations across value categories and challenges in mitigating discrepancies.
We explore the alignment of values in Large Language Models (LLMs) with specific age groups, leveraging data from the World Value Survey across thirteen categories. Through a diverse set of prompts tailored to ensure response robustness, we find a general inclination of LLM values towards younger demographics, especially when compared to the US population. Although a general inclination can be observed, we also found that this inclination toward younger groups can be different across different value categories. Additionally, we explore the impact of incorporating age identity information in prompts and observe challenges in mitigating value discrepancies with different age cohorts. Our findings highlight the age bias in LLMs and provide insights for future work. Materials for our analysis are available at \url{ https://github.com/MichiganNLP/Age-Bias-In-LLMs}