Dynamic fairness-aware recommendation through multi-agent social choice
This work addresses fairness challenges in recommender systems for users and stakeholders, offering a novel formalization that goes beyond classification-based approaches, though it appears incremental in building on social choice theory.
The paper tackles the problem of algorithmic fairness in personalized recommendation systems by proposing a two-stage social choice model that integrates multiple fairness concerns dynamically, with simulations demonstrating the framework's capability to handle these complex requirements.
Algorithmic fairness in the context of personalized recommendation presents significantly different challenges to those commonly encountered in classification tasks. Researchers studying classification have generally considered fairness to be a matter of achieving equality of outcomes between a protected and unprotected group, and built algorithmic interventions on this basis. We argue that fairness in real-world application settings in general, and especially in the context of personalized recommendation, is much more complex and multi-faceted, requiring a more general approach. We propose a model to formalize multistakeholder fairness in recommender systems as a two stage social choice problem. In particular, we express recommendation fairness as a novel combination of an allocation and an aggregation problem, which integrate both fairness concerns and personalized recommendation provisions, and derive new recommendation techniques based on this formulation. Simulations demonstrate the ability of the framework to integrate multiple fairness concerns in a dynamic way.