AUDITING THE CLOSED IOS ECOSYSTEM: IS THERE POTENTIAL FOR LARGE LANGUAGE MODEL APP INSPECTIONS?

Resumen

Scholarly attention is increasingly paid to the dynamic and embedded ways in which third-party tracking has become widespread in the mobile ecosystem. Much of this research has focused almost exclusively on Android applications in Google Play and their respective infrastructures of data capture, even though Apple and their App Store have a significant market share. To examine Apple’s ecosystem and to overcome the challenge of opening and inspecting large quantities of apps, we have explored the role that a large language model (LLM) can play in enabling this process? We have opted to use Chat GPT-4 because of how it has been designed to assist humans in both interacting and understanding code. We therefore ask: Can Chat-GPT assist scholars interested in Apple’s mobile ecosystem through the auditing of Apple app (IPA) files? The outcomes of the explorative study include 1) an evaluation of Chat-GPT4 as an assistant for reading code at scale as well as its potential for automating processes involved in future app monitoring and regulation; 2) a deep dive into the shortcomings of Chat-GPT4 when it comes to interventions into the mobile ecosystems; and 3) a discussion of ethical issues involved in harnessing LLM for forwarding a research agenda around Apple’s otherwise understudied ecosystem. Our exploration aims to evaluate a methodological intervention that could bring more observability into an otherwise closed ecosystem that promises the preservation of end-user privacy, without any meaningful external oversight.