Dr. Jennifer Pybus: Auditing Extractive Infrastructures of Datafication in Mobile Applications

This workshop introduces an infrastructural mixed method for auditing how health apps embed third-party software development kits (SDKs) and access both personal and health-related data from mobile devices.

The method includes:

• An app selection based on relevant criteria
• An assisted manifest data audit using a large language model (LLM)
• A qualitative examination of corresponding privacy policies and data safety agreements
• A walkthrough method, established by Light et al. (2018), to account for the different kinds of health and personal data that can be input in the apps’ interface.

Rather than focusing primarily on user behaviour or consent, the method centres on qualitative analysis of Android manifest files, since any personal data an application seeks to access from a user’s device, or share with third parties, should be declared there. Participants will also be introduced to the role app events play in personal data tracking, and to how health-related data are structured in manifest files in ways that make them legible and reusable across a range of actors, including large platforms.

By the end of the session, participants will have a clearer understanding of how to conduct a static audit of mobile tracking infrastructures and compare back-end findings with front-end privacy policies in order to better infer how personal and health data are extracted, shared, and monetised through third-party SDKs, and how these practices are, or are not, communicated to end users.

🚨 Important: Participants must pre-install software tools in advance of the workshop. Please register early to obtain the installation instructions and recommended pre-reading. Places are limited.

🎟️ Register for the workshop by sending an email to digslab@concordia.ca with your name, department, and level of study.

ABOUT JENNIFER PYBUS:

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.

This event is supported by the Canada Research Chair in Data, Democracy and AI, the Digital Intimacy, Gender and Sexuality Lab, and the Speculative Life cluster at Milieux.

February 25, 2026

2:30 – 4 PM

Speculative Life Cluster Room EV 10.625