Fairness Vs. Personalization: Towards Equity in Epistemic Utility

TL;DR

This paper explores the conflict between personalization and fairness in recommender systems, proposing equity as a means to balance individual utility with fairness, and offers practical policy recommendations.

Contribution

It introduces the concept of equity as an alternative fairness measure in personalized systems and maps goals to implementations for achieving epistemic fairness.

Findings

Highlights the tension between personalization and fairness

Proposes equity as a novel fairness approach

Provides policy recommendations for stakeholders

Abstract

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.