Where Should I Study? Biased Language Models Decide! Evaluating Fairness in LMs for Academic Recommendations
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866908646794854400 |
|---|---|
| author | Shailya, Krithi Mishra, Akhilesh Kumar Krishnan, Gokul S Ravindran, Balaraman |
| author_facet | Shailya, Krithi Mishra, Akhilesh Kumar Krishnan, Gokul S Ravindran, Balaraman |
| contents | Large Language Models (LLMs) are increasingly used as daily recommendation systems for tasks like education planning, yet their recommendations risk perpetuating societal biases. This paper empirically examines geographic, demographic, and economic biases in university and program suggestions from three open-source LLMs: LLaMA-3.1-8B, Gemma-7B, and Mistral-7B. Using 360 simulated user profiles varying by gender, nationality, and economic status, we analyze over 25,000 recommendations. Results show strong biases: institutions in the Global North are disproportionately favored, recommendations often reinforce gender stereotypes, and institutional repetition is prevalent. While LLaMA-3.1 achieves the highest diversity, recommending 481 unique universities across 58 countries, systemic disparities persist. To quantify these issues, we propose a novel, multi-dimensional evaluation framework that goes beyond accuracy by measuring demographic and geographic representation. Our findings highlight the urgent need for bias consideration in educational LMs to ensure equitable global access to higher education. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_04498 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Where Should I Study? Biased Language Models Decide! Evaluating Fairness in LMs for Academic Recommendations Shailya, Krithi Mishra, Akhilesh Kumar Krishnan, Gokul S Ravindran, Balaraman Computation and Language Artificial Intelligence Large Language Models (LLMs) are increasingly used as daily recommendation systems for tasks like education planning, yet their recommendations risk perpetuating societal biases. This paper empirically examines geographic, demographic, and economic biases in university and program suggestions from three open-source LLMs: LLaMA-3.1-8B, Gemma-7B, and Mistral-7B. Using 360 simulated user profiles varying by gender, nationality, and economic status, we analyze over 25,000 recommendations. Results show strong biases: institutions in the Global North are disproportionately favored, recommendations often reinforce gender stereotypes, and institutional repetition is prevalent. While LLaMA-3.1 achieves the highest diversity, recommending 481 unique universities across 58 countries, systemic disparities persist. To quantify these issues, we propose a novel, multi-dimensional evaluation framework that goes beyond accuracy by measuring demographic and geographic representation. Our findings highlight the urgent need for bias consideration in educational LMs to ensure equitable global access to higher education. |
| title | Where Should I Study? Biased Language Models Decide! Evaluating Fairness in LMs for Academic Recommendations |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2509.04498 |