Fairness for niche users and providers: algorithmic choice and profile portability
It addresses fairness for niche stakeholders in recommender systems by examining structural changes like algorithmic pluralism, which is an incremental extension of prior simulation work.
The paper investigates how profile portability policies affect fairness outcomes for niche consumers and providers in recommender systems, using simulation to explore interactions between user choice in algorithms and structural ecosystem changes.
Ensuring fair outcomes for multiple stakeholders in recommender systems has been studied mostly in terms of algorithmic interventions: building new models with better fairness properties, or using reranking to improve outcomes from an existing algorithm. What has rarely been studied is structural changes in the recommendation ecosystem itself. Our work explores the fairness impact of algorithmic pluralism, the idea that the recommendation algorithm is decoupled from the platform through which users access content, enabling user choice in algorithms. Prior work using a simulation approach has shown that niche consumers and (especially) niche providers benefit from algorithmic choice. In this paper, we use simulation to explore the question of profile portability, to understand how different policies regarding the handling of user profiles interact with fairness outcomes for consumers and providers.