Invigorating Competition in Social Networking: An Interoperability Remedy that Addresses Data Network Effects and Privacy Concerns

Abstract

The persistent dominance of Facebook has led many scholars and policymakers to generate proposals to invigorate competition in social networking. In this piece we address a remedy that has received renewed attention: interoperability. Prior proposals of interoperability have focused on eroding entry barriers that exist due to user-based network effects. We focus here on data-generated network effects: the more data Facebook acquires from its users, the more its AI algorithms can learn and improve the content Facebook provides its users. Without access to a rich stream of user data, a social network is merely a static interface, with limited capacity to serve engaging or personalized content. As such, we propose a version of interoperability that addresses both user and data-driven network effects. In doing so, we also explicitly tackle the privacy issues that invariably arise whenever data is shared across firms.

Shayne Longpre

Applied ML Scientist (NLP)

My research interests include AI/ML/NLP, and the governance of AI platforms.