The policy states that TikTok uses collected data including user content, messages, AI interactions, and metadata to train and improve machine learning models and algorithms, and that this includes scanning and analyzing user content and messages.
This analysis describes what TikTok's agreement states, permits, or reserves. It does not constitute a legal determination about enforceability. Regulatory applicability and practical outcomes may vary by jurisdiction, enforcement context, and individual circumstances. Read our methodology
This provision authorizes use of user-generated content, direct messages, and AI interaction data for machine learning training purposes, which extends the use of this data beyond service delivery into technology development. The inclusion of messages in the scope of ML training data is operationally notable.
⚠ User content, messages, and AI interaction data will be used for ML model training under the terms as written
Cross-platform context
See how other platforms handle Machine Learning Model Training Using User Data and similar clauses.
Compare across platforms →"To review, support, improve, and develop the Services, and train, test, and improve technology, such as machine learning models and algorithms, including by analyzing how people are using the Services; conducting research; soliciting feedback; monitoring your activity and usage across the devices you use to access the Services; and scanning, analyzing, and reviewing user content, messages, AI interactions, and associated metadata.Excerpt from TikTok's Privacy Policy
(1) REGULATORY LANDSCAPE: Use of user-generated content and messages for ML training engages CCPA purpose limitation principles and may implicate the California Privacy Rights Act's provisions on automated decision-making.
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This provision authorizes use of user-generated content, direct messages, and AI interaction data for machine learning training purposes, which extends the use of this data beyond service delivery into technology development. The inclusion of messages in the scope of ML training data is operationally notable.
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