Fairness Vs. Personalization: Towards Equity in Epistemic Utility

Personalized recommender systems are rapidly expanding in applications across the web, encompassing social media, online shopping, search engine results, and more. Personalization provides significant value in navigating the vast array of items available, as it can help to disambiguate search terms and generally provide more accurate recommendations. However, alongside this growth, there has been increased recognition of the potential for algorithmic systems to exhibit and perpetuate biases. In particular, personalized recommender systems may risk unfairness in recommendations, leading to biases in access to information and epistemic utility. In this work, we differentiate between idealized and realized notions of personalization in the extreme to motivate our fairness definition: an equity-based approach to epistemic utility. Our approach bypasses many issues with comparative notions of fairness and instead aims to guarantee all users baseline access to information. We discuss key trade-offs and challenges for operationalizing our definition and provide policy recommendations towards achieving fairness in personalized systems.