Stairway to Fairness: Connecting Group and Individual Fairness

Abstract

Fairness in recommender systems (RSs) is commonly categorised into group fairness and individual fairness. However, there is no established scientific understanding of the relationship between the two fairness types, as prior work on both types has used different evaluation measures or evaluation objectives for each fairness type, thereby not allowing for a proper comparison of the two. As a result, it is currently not known how increasing one type of fairness may affect the other. To fill this gap, we study the relationship of group and individual fairness through a comprehensive comparison of evaluation measures that can be used for both fairness types. Our experiments with 8 runs across 3 datasets show that recommendations that are highly fair for groups can be very unfair for individuals. Our finding is novel and useful for RS practitioners aiming to improve the fairness of their systems. Our code is available at: https://github.com/theresiavr/stairway-to-fairness.

Accepted to RecSys 2025 (short paper)

References in corpus (14)

• A Survey on the Fairness of Recommender Systems
• Fairness in Recommender Systems: Research Landscape and Future Directions
• FairRec: Two-Sided Fairness for Personalized Recommendations in Two-Sided Platforms
• Is ChatGPT Fair for Recommendation? Evaluating Fairness in Large Language Model Recommendation
• Large Language Models as Zero-Shot Conversational Recommenders
• Fighting Fire with Fire: Using Antidote Data to Improve Polarization and Fairness of Recommender Systems
• Evaluation Measures of Individual Item Fairness for Recommender Systems: A Critical Study
• Principled Multi-Aspect Evaluation Measures of Rankings
• Intersectional Two-sided Fairness in Recommendation
• RecAI: Leveraging Large Language Models for Next-Generation Recommender Systems
• It's Not You, It's Me: The Impact of Choice Models and Ranking Strategies on Gender Imbalance in Music Recommendation
• Efficient and Joint Hyperparameter and Architecture Search for Collaborative Filtering
• Can We Trust Recommender System Fairness Evaluation? The Role of Fairness and Relevance
• Properties of Group Fairness Metrics for Rankings