The Personality Trap: How LLMs Embed Bias When Generating Human-Like Personas

TL;DR

This study investigates biases in large language models when generating human-like personas, revealing inherent sociodemographic stereotypes and potential risks of stereotyping marginalized groups.

Contribution

It provides a comprehensive analysis of biases in LLM-generated personas and explores how input traits influence demographic and identity representations.

Findings

LLMs reproduce known personality-sociodemographic correlations

Pronounced WEIRD biases favoring Western, educated, and affluent individuals

Maximizing certain traits leads to overrepresentation of marginalized identities

Abstract

This paper examines biases in large language models (LLMs) when generating synthetic populations from responses to personality questionnaires. Using five LLMs, we first assess the representativeness and potential biases in the sociodemographic attributes of the generated personas, as well as their alignment with the intended personality traits. While LLMs successfully reproduce known correlations between personality and sociodemographic variables, all models exhibit pronounced WEIRD (western, educated, industrialized, rich and democratic) biases, favoring young, educated, white, heterosexual, Western individuals with centrist or progressive political views and secular or Christian beliefs. In a second analysis, we manipulate input traits to maximize Neuroticism and Psychoticism scores. Notably, when Psychoticism is maximized, several models produce an overrepresentation of non-binary and LGBTQ+ identities, raising concerns about stereotyping and the potential pathologization of marginalized groups. Our findings highlight both the potential and the risks of using LLMs to generate psychologically grounded synthetic populations.