Regulating Artificial Intelligence Intimacies: The Miseducation of South Korean AI Chatbot Iruda
Bibliographic record
Abstract
Background: The AI companion “Iruda” became the first case in South Korea where a tech company was fined under the Personal Information Protection Act (PIPA), raising critical concerns about synthetic media and their role in data extraction through parasocial intimacy. Analysis: Drawing on the Iruda case, this article analyzes the ethical and privacy implications of synthetic media in a Canadian context, highlighting where regulatory gaps persist, with the Artificial Intelligence and Data Act (AIDA) lacking clear definitions of “high impact” AI systems and the Online Harms Act excluding most chatbot products. Conclusions and Implications: The Iruda case emphasizes the urgency of developing targeted regulations for synthetic media in Canada, ensuring frameworks like AIDA and the Online Harms Act adequately address risks tied to the extraction of parasocial data.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".