Fairness Vs. Personalization: Towards Equity in Epistemic Utility
This tackles fairness issues in personalized systems like social media and online shopping, but it is incremental as it builds on existing fairness concepts.
The paper addresses the tension between personalization and fairness in recommender systems, proposing equity as an alternative framework to achieve fairness in epistemic utility, with policy recommendations for stakeholders.
The applications of personalized recommender systems are rapidly expanding: encompassing social media, online shopping, search engine results, and more. These systems offer a more efficient way to navigate the vast array of items available. However, alongside this growth, there has been increased recognition of the potential for algorithmic systems to exhibit and perpetuate biases, risking unfairness in personalized domains. In this work, we explicate the inherent tension between personalization and conventional implementations of fairness. As an alternative, we propose equity to achieve fairness in the context of epistemic utility. We provide a mapping between goals and practical implementations and detail policy recommendations across key stakeholders to forge a path towards achieving fairness in personalized systems.