“Trust Me Over My Privacy Policy”: Privacy Discrepancies in Romantic AI Chatbot Apps
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Artificial intelligence (AI) is being pervasively integrated into various facets of human life, including the emotional realm. Romantic AI chatbots, positioned as artificial companions offering emotional support and connection, have witnessed a significant rise in recent years. Users of romantic AI chatbots often reveal personal information during intimate conversations, potentially unaware of the consequences or how their data may be utilized. Complicating matters, lengthy and convoluted privacy policies are commonly overlooked or misunderstood by users. This study aims to address these privacy concerns by introducing a comprehensive framework for analyzing the privacy practices of romantic AI chatbot apps. Through a combination of static and dynamic analysis, we investigate 21 Android romantic AI chatbot apps for: discrepancies between privacy policies and chatbot responses to questions regarding privacy practices; social login and age verification mechanisms; permissions requested by apps; data sharing practices; tracking services employed; and potential security vulnerabilities. Our findings highlight the prevalence of discrepancies between chatbot responses regarding users' privacy and the privacy policies of the apps. Additionally, we note some concerning observations related to: customer service responses to privacy concerns; inadequate age verification measures; contradictions in data sharing claims; and extensive usage of tracking services. We found that all romantic AI chatbot apps tested had discrepancies between their chatbots' responses and privacy policies. None of the apps take any measures against faking the birthdate, and most would continue the conversation despite knowing that the user is underage. 13 out of 21 romantic AI chatbot apps use at least 3 tracking services, and 18 out of 21 apps send detailed device information to tracking services. This study reveals privacy and security flaws in romantic AI chatbot apps, stressing the need for better transparency and user protection measures. Particularly, Discrepancies between chatbot responses and privacy policies highlight the importance of clear communication on data handling.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it