# Concerns on Bias in Large Language Models when Creating Synthetic Personae

arXiv: https://arxiv.org/abs/2405.05080 · HTML: https://mlsanity.com/paper/2405.05080/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 18 references that are in the map.
- First posted on arXiv 8 May 2024.

## Builds on (most cited first, 3 of 3)

- ["Kelly is a Warm Person, Joseph is a Role Model": Gender Biases in LLM-Generated Reference Letters](https://mlsanity.com/paper/2310.09219/) (2023)
- [Auditing large language models: a three-layered approach](https://mlsanity.com/paper/2302.08500/) (2023)
- [Qualitative Analysis for Human Centered AI](https://mlsanity.com/paper/2112.03784/) (2021)

## Built on by (most cited first, 3 of 3)

- [A Tale of Two Identities: An Ethical Audit of Human and AI-Crafted Personas](https://mlsanity.com/paper/2505.07850/) (2025)
- [Localizing Persona Representations in LLMs](https://mlsanity.com/paper/2505.24539/) (2025)
- [Making Sense of AI Limitations: How Individual Perceptions Shape Organizational Readiness for AI Adoption](https://mlsanity.com/paper/2502.15870/) (2025)

## Abstract (arXiv)

This position paper explores the benefits, drawbacks, and ethical considerations of incorporating synthetic personae in HCI research, particularly focusing on the customization challenges beyond the limitations of current Large Language Models (LLMs). These perspectives are derived from the initial results of a sub-study employing vignettes to showcase the existence of bias within black-box LLMs and explore methods for manipulating them. The study aims to establish a foundation for understanding the challenges associated with these models, emphasizing the necessity of thorough testing before utilizing them to create synthetic personae for HCI research.