Dynamic Schedule Planner: Automated Workload Balancing via Syllabus and Canvas Extraction; Ethical Data Collection in Big Tech: How Digital Consent is Designed to Fail 7 views

Everyday, billions of people click "I Agree" without reading a word of what they are consenting to, and that is not an accident. In a time where personal data fuels the explosive growth of artificial intelligence, the design of digital consent has become one of the most important yet least examined ethical problems in tech. This project investigates how Big Tech platforms deliberately engineer consent systems to extract user data while maintaining just enough legal cover to avoid real accountability. The main problem my research addresses is the gap between legal compliance and actual ethical consent. Major tech companies like Google and Meta have built consent interfaces that technically satisfy regulations but ignore the conditions that make consent meaningful in the first place. This paper takes a close look at the Brown v. Google LLC incognito tracking case and a recent Android data collection lawsuit. Just these two examples are enough to expose the common practices and nature of data collection from modern tech firms, as I record how design choices like asymmetric button sizing, implied anonymity, and hidden opt-out flows push users into making decisions they do not fully understand. To make it worse, the financial penalties companies face, if luckily caught, remain a small fraction of the revenue generated by these very practices, creating a clear incentive to keep this system exactly as it has been for years. Understanding this from a technical aspect is only half the picture. Consent does not happen in a vacuum, and the human and social dimensions of interface design can not be separated from the technical ones. To dig into these dimensions, this research applies two STS frameworks: Actor Network Theory (ANT) and Helen Nissenbaum's contextual integrity. ANT shows that a single consent click is not just a personal decision but a network outcome shaped by engineers, legal teams, algorithms, regulators, and cultural norms all working together. This also explains why reform is so hard since no single actor can be held fully responsible. Contextual integrity adds that even obtained consent falls short when data flows outside what users reasonably expected, like location data shared with a navigation app ending up in advertising profiles. Together, these frameworks reframe the core issue that digital consent is not failing because users are careless. It is failing because the systems were built to produce that outcome and exploit unsuspecting users. When considered together, both sides of this research point to the same conclusion. Consent as it is currently designed is not a real ethical safeguard but a mechanism for data extraction dressed up as one. Fixing this requires rethinking who these systems are actually built to serve.

BS (Bachelor of Science)

Digital consent; Dark patterns; Data privacy; Surveillance capitalism; Big Tech ethics

School of Engineering and Applied Science Bachelor of Science in Computer Science Technical Advisor: Mark Sherriff STS Advisor: Sean Murray Technical Team Members: Edward Cho-Jung, Chang Huang, Tapi Goredema

English

All rights reserved by the author (no additional license for public reuse)

2026-05-08