Platform Monopolisation: Inside the Permissive Mobile Ecosystem

Please note this event has been rescheduled from March 23rd to March 30th.

Abstract

This talk draws on AHRC funded research that has investigated how Facebook and Google have expanded their quasi-monopolies by operating inside the majority of popular applications. On the one hand, these platforms operate as discrete entities that facilitate the participation of billions of users; on the other hand, they act as distributed agents, wherein they are reconfigured to provide a number of different services for app developers. I will therefore begin by demonstrating how large platforms like Facebook and Google exist as both first parties and as third parties via a new service economy model that is enabling a much deeper technical integration—something that has been overlooked and under-researched. The contention is that this new configuration of the mobile ecosystem, predicated on embedded third party services is enabling a more mature phase of datafication. Finally, I will present our methodological approach, which involved the creation of a platform that holds over 7000 mobile applications, aimed at understanding the digital objects that facilitate the capture, accumulation, movement and (re)use of personal data within Android’s infrastructure. Our interdisciplinary and experimental design demonstrates how we researched technical objects of datafication—objects that transform sociality into data—namely: i) Software Development Kits (SDKs) and ii) manifest permissions which govern privacy permissions. As I will conclude, this interrelationship is enabling Google and Facebook to create more opportunities to expand their dominance within the personal data economy.

Speaker Biography

Dr. Jennifer Pybus is a Canada Research Chair (Tier 2) in Data, Democracy and AI, and Director of York’s new Centre for Public AI. Her interdisciplinary research intersects digital and algorithmic cultures by exploring the capture and processing of personal data. Her research contributes to an emerging field of datafication, a process that is rendering our social, cultural and political lives into productive data for machine learning and algorithmic decision-making. Her focus engages communities to open up new explainable approaches for understanding the impact of using personal data in AI technologies.

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