@inproceedings{alshabanah-annavaram-2025-mind,
    title = "Mind the Dialect: {NLP} Advancements Uncover Fairness Disparities for {A}rabic Users in Recommendation Systems",
    author = "Alshabanah, Abdulla  and
      Annavaram, Murali",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.findings-emnlp.860/",
    doi = "10.18653/v1/2025.findings-emnlp.860",
    pages = "15895--15903",
    ISBN = "979-8-89176-335-7",
    abstract = "Recommendation systems play a critical role in shaping user experiences and access to digital content. However, these systems can exhibit unfair behavior when their performance varies across user groups, especially in linguistically diverse populations. Recent advances in NLP have enabled the identification of user dialects, allowing for more granular analysis of such disparities. In this work, we investigate fairness disparities in recommendation quality among Arabic-speaking users, a population whose dialectal diversity is underrepresented in recommendation system research. By uncovering performance gaps across dialectal variation, we highlight the intersection of NLP and recommendation system and underscore the broader social impact of NLP. Our findings emphasize the importance of interdisciplinary approaches in building fair recommendation systems, particularly for global and local platforms serving diverse Arabic-speaking communities. The source code is available at https://github.com/alshabae/FairArRecSys."
}