Fairness Vs. Personalization: Towards Equity in Epistemic Utility
Jennifer Chien David Danks
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.