DeepL analyses translation texts to improve its services, but claims to anonymise data before doing so. Paid DeepL Pro subscribers' content is specifically not used to train the translation AI.
This analysis describes what DeepL'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
The provision establishes the operational parameters for data use in service improvement, distinguishing between anonymized analysis permitted for all users and training data use restricted for Pro subscribers. This creates a differential data treatment framework based on subscription tier.
Free users' translation content is subject to anonymised analysis for AI improvement purposes, while DeepL Pro subscribers' texts are explicitly excluded from model training — meaning the privacy protection for translation data depends on which subscription tier you use.
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We are simplifying our Terms of Use, including clarifications around the use of AI tools, and their data use. We have moved the terms that describe AI Features, which were previously written for a Creator audience and located under the AI-Based Tools Supplemental Terms and Disclaimer, into the User ...
Data publicly available on the Internet. Our artificial intelligence models are trained on data that is publicly available on the Internet by third parties, which may contain personal data, even if we use good practices to filter out such personal data. [...] Training Datasets. In some cases, we acc...
We may use aggregated, de-identified data derived from your use of our services, including document metadata and usage patterns, to develop, train, and improve our artificial intelligence and machine learning models and product features.
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"To improve the quality of our services, we analyse texts submitted for translation. We ensure that this analysis cannot be traced back to individual users by anonymising the data before analysis. DeepL Pro subscribers' texts are not used to train our machine translation systems.— Excerpt from DeepL's DeepL Terms and Conditions
REGULATORY FRAMEWORK: This provision engages GDPR Art. 5(1)(b) (purpose limitation), Art. 5(1)(c) (data minimisation), and Art. 89 (safeguards for processing for scientific or statistical purposes). The key question is whether anonymisation is effective under GDPR Recital 26 — if data can be re-identified, GDPR obligations apply in full. The EU AI Act (Regulation 2024/1689) may impose additional obligations regarding training data governance. Enforcement authority is the German BfDI and national DPAs for EU users; the ICO for UK users.
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The provision establishes the operational parameters for data use in service improvement, distinguishing between anonymized analysis permitted for all users and training data use restricted for Pro subscribers. This creates a differential data treatment framework based on subscription tier.
Free users' translation content is subject to anonymised analysis for AI improvement purposes, while DeepL Pro subscribers' texts are explicitly excluded from model training — meaning the privacy protection for translation data depends on which subscription tier you use.
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