Can LLMs Contribute to Social Inclusion? A Zero-Shot Analysis of Homelessness Bias Detection on Reddit

Jonathan A. Karr Jr., Ben Herbst, Matthew Hauenstein, Georgina Curto, Nitesh V Chawla

Keywords: large language models, natural language processing, people experiencing homelessness

TL;DR: Our research tackles the issue of biases in online discussions about homelessness by using LLMs to analyze Reddit data from 10 U.S. cities.

Abstract: Homelessness is a persistent social challenge, impacting millions worldwide. Over 770,000 people experienced homelessness in the U.S. in 2024. Social stigmatization is a significant barrier to alleviation, shifting public perception, and influencing policy. Online discourse on platforms such as Reddit shape public opinion. We present novel methods that build on natural language processing (NLP) and large language models (LLMs) research to mitigate bias against people experiencing homelessness (PEH) in online spaces. We gather Reddit data for 10 U.S. cities, then perform zero-shot classification, and finally, we apply mitigation techniques using Llama 3.2 Instruct and Qwen 2.5 7B Instruct models. The results highlight the inconsistencies between LLMs when used to classify homelessness bias and the low effectiveness of GenAI tools to mitigate PEH online. The ultimate goal of this work is to promote awareness on bias against PEH, produce new indicators that inform policy, and improve the fairness of GenAI.

Archival Status: Non-archival

Paper Length: Long Paper (up to 8 pages of content)

Submission Number: 328