Online Platforms and the Fair Exposure Problem Under Homophily

Jakob Schoeffer*, Alexander Ritchie, Keziah Naggita, Faidra Monachou, Jessie Finocchiaro, Marc Juarez Miro

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

In the wake of increasing political extremism, online platforms have been criticized for contributing to polarization. One line of criticism has focused on echo chambers and the recommended content served to users by these platforms. In this work, we introduce the fair exposure problem: given the limited intervention power of the platform, the goal is to enforce balance in the spread of content (e.g., news articles) among two groups of users through constraints similar to those imposed by the Fairness Doctrine in the United States in the past. Groups are characterized by different affiliations (e.g., political views) and have different preferences for content. We develop a stylized framework that models intra- and inter-group content propagation under homophily, and we formulate the platform's decision as an optimization problem that aims at maximizing user engagement, potentially under fairness constraints. Our main notion of fairness requires that each group see a mixture of their preferred and non-preferred content, encouraging information diversity. Promoting such information diversity is often viewed as desirable and a potential means for breaking out of harmful echo chambers. We study the solutions to both the fairness-agnostic and fairness-aware problems. We prove that a fairness-agnostic approach inevitably leads to group-homogeneous targeting by the platform. This is only partially mitigated by imposing fairness constraints: we show that there exist optimal fairness-aware solutions which target one group with different types of content and the other group with only one type that is not necessarily the group's most preferred. Finally, using simulations with real-world data, we study the system dynamics and quantify the price of fairness.
Original languageEnglish
Title of host publicationProceedings of the 37th AAAI Conference on Artificial Intelligence
PublisherAAAI Press
Pages11899-11908
Volume37
Edition10
DOIs
Publication statusPublished - 26 Jun 2023
EventThe 37th AAAI Conference on Artificial Intelligence - Walter E. Washington Convention Center, Washington, United States
Duration: 7 Feb 202314 Feb 2023
https://aaai.org/Conferences/AAAI-23/

Publication series

NameProceedings of the AAAI Conference on Artificial Intelligence.
PublisherAAAI Press
Number10
Volume37
ISSN (Print)2159-5399
ISSN (Electronic)2374-3468

Conference

ConferenceThe 37th AAAI Conference on Artificial Intelligence
Abbreviated titleAAAI-23
Country/TerritoryUnited States
CityWashington
Period7/02/2314/02/23
Internet address

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