Mind the Dialect: NLP Advancements Uncover Fairness Disparities for A rabic Users in Recommendation Systems (2025)

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.

## introduction recommendation systems shape the way we interact with digital content and, by extension, influence how we perceive the world around us in the digital age. from the content provider's perspective, they have become integral to online platforms, enhancing user experience through personalization @xcite and driving significant economic activity for major technology companies @xcite @xcite . given their societal impact, it is critical to uncover biases that could negatively affect user experience or undermine equitable access to content. one unexplored source of bias in recommendation system research is related to arabic-speaking users. arabic, spoken by over 420 million people worldwide, comprises numerous dialects that differ significantly in grammar and vocabulary @xcite . while these dialects share a common root in modern standard arabic (msa), they have diverged across regions, often resulting in limited mutual intelligibility. this divergence creates a form of diglossia, where msa is used as the formal, high-resource language, while regional dialects are less represented in digital and formal contexts @xcite . recent work and shared tasks in arabic nlp has focused primarily on dialect identification @xcite @xcite , with only one study exploring the use of dialect information to improve recommendation performance @xcite . these advancements in arabic nlp have made it possible to examine a critical fairness issue: the quality of recommendations for arabic-speaking users varies with the dialect they use. our analysis shows that users using certain dialects receive lower-quality recommendations, leading to access disparities across dialect groups. in this paper, we take a first step toward uncovering this fairness issue in recommendation systems for arabic-speaking users. our findings challenge the implicit but unexamined assumption that all arabic speakers are treated equally by recommendation systems. by highlighting disparities tied to arabic dialects, this work draws attention to the broader impact of nlp and its connection to other fields. ## conclusion we uncover a fairness issue in recommendation systems for arabic-speaking users, showing that performance varies significantly by dialect. our analysis highlights the importance of accounting for linguistic diversity and show that nlp advancements can reveal hidden disparities in related fields.