Super SDKs: Tracking Personal Data and Platform Monopolies in the Mobile Ecosystem

This presentation focuses on the question ‘what is tracking’ through a comprehensive overview of what Software Development Kit (SDKs) are and why these technical objects are relevant to platform power. The aim is to examine the relationship between personal data capture and platform monopolisation through a socio-technical overview of how datafication works in our apps.  SDKs are important tools used by developers to make apps work and to capitalise on their users. The average person has more than 40 different apps on their phone and each app uses an average of 18 SDKs which gather, share, and process our data. Users have little access to or understanding of this core technical data hub or of how it is supercharging profits for tech giants like Google or Facebook. Nor do Humanities scholars possess many tools or methods for examining the role that SDKs play in linking user data that gets generated on mobile devices with our app’s, with third parties, and with platforms. To address this research problem, I will present the SDK taxonomy that we have developed. We see this as a preliminary road map for advancing research on the expansive number of services offered to developers to build and monetise their apps. Finally, I will raise a series of related regulatory privacy challenges embedded in our apps to the raise the question the question of why the small of the SDK is much bigger than the whole of the platform?

Jennifer Pybus is a globally recognized scholar whose interdisciplinary research intersects digital and algorithmic cultures and explores the capture and processing of personal data. Her work focuses on the political economy of social media platforms, display ad economies, and the rise of third parties embedded in the mobile ecosystem which are facilitating algorithmic profiling, monetisation, polarization and bias. Her research contributes to an emerging field, mapping out datafication, a process that is rendering our social, cultural and political lives into productive data for machine learning and algorithmic decision-making. Pybus has cultivated strong European links with public organizations and will use her chair to engage Canadians with innovative tools, resources and pedagogy for increasing critical data literacy and democratic debate about artificial intelligence.