Navigating Instagram's compliance with transparency requirements in EU and Canadian data protection law
Bibliographic record
Abstract
This thesis investigates social media platform compliance with transparency requirements under the European Union’s General Data Protection Regulation (GDPR) and Canada’s Privacy and AI Act. It explores the challenges posed by automated decision-making and data-driven algorithms, particularly regarding user privacy, consent, and transparency obligations. Using Instagram as a case study, the research evaluates its privacy policies, consent mechanisms, and data collection practices. The analysis highlights gaps in Instagram’s approach to transparency, focusing on algorithmic profiling, personalized advertising, and content recommendations. It expands the analysis to other social media platforms, including Facebook, TikTok, and YouTube, underscoring broader compliance issues in the industry. The thesis proposes recommendations to strengthen transparency through clearer privacy policies, improved consent mechanisms, and enhanced enforcement of legal standards. It also advocates for adopting user-centric approaches, including simplified language and privacy dashboards, to empower individuals to make informed decisions about their data. These findings contribute to the broader discourse on data governance and ethical AI use, offering insights for regulators, policymakers, and platform developers to promote privacy accountability in the digital age
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.056 | 0.122 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.026 | 0.015 |
| Scholarly communication | 0.024 | 0.007 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".