How ChatGPT learns about the world while protecting privacy

How ChatGPT learns about the world while protecting privacy | OpenAI

May 6, 2026

How ChatGPT learns about the world while protecting privacy

A plain-language guide to model training, privacy safeguards, and the privacy choices available in ChatGPT.

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Editor's note for Canada: The French text follows the English text (Le texte français suit le texte anglais).

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ChatGPT is becoming more capable across domains, helping people with complex, real-world work like coding, research, analysis, and multi-step tasks across tools. Those gains in capability are driven by training on a wide variety of data to help our models build broad knowledge of the world and apply it to new tasks.

As OpenAI continues to develop frontier models, we work hard to help ensure that our model training process respects privacy. We’ve developed state of the art technologies to help our models learn useful general patterns rather than private information about individuals, and we have a number of user controls and policies to help keep individuals in control of their data.

This post explains what information may be used in model training, how we reduce the processing of personal information in that process, and how users can control whether their ChatGPT conversations help improve our models.

What information may be used in training

To develop the models that power ChatGPT⁠, we use a mix of information sources, including publicly available information, information we access through partnerships, and information provided or generated by users, contractors, and researchers. This data helps models build general knowledge and respond more reliably and safely.

For publicly available internet content, we use only information that is freely and openly accessible. For example, if you participate in a publicly available online discussion forum, or post a blog or other public post, we may use that publicly accessible content for model training purposes.

How we reduce personal information in training

Before information is used in training, we apply safeguards designed to reduce personal information in our datasets. One of those safeguards is OpenAI Privacy Filter, which identifies and masks personal information in text. In our evaluations, Privacy Filter is more effective at removing personal information than any other tool of its kind.

We use an internal version of Privacy Filter at multiple stages in the training process, including on public datasets that we use for training, as well as on user conversations if they have “Improve the model for everyone” enabled.

We have also made Privacy Filter available to other developers for free⁠, to help the broader industry protect privacy in their workflows.

Privacy controls in ChatGPT

Users can choose whether their conversations with ChatGPT help train future models. Users can go to Settings, then Data Controls, and turn off "Improve the model for everyone.⁠” Once this setting is off, new conversations still appear in chat history but are not used to train ChatGPT.

Temporary Chat⁠ offers another option. To start one, open a new chat and click the "Temporary" button in the top-right corner of the page. Temporary Chats do not appear in chat history, do not create memories, and are not used to improve our models. Conversations are retained for 30 days for safety purposes, and are then deleted.

In addition, Memory⁠ makes ChatGPT’s responses more useful by remembering things you don’t want to keep reminding ChatGPT—like important people in your life, projects you’re working on, or topics you usually ask about. It’s always optional: you can review, edit, or delete saved memories, or turn memory off entirely. When it’s off, ChatGPT won’t save or reference memory from past chats.

Users can also export their ChatGPT data, delete their account, manage data controls from settings, and submit privacy requests through the privacy request portal⁠. Users should not share sensitive information in ChatGPT that they wouldn't want to be used or reviewed.

Preserving privacy in responses

ChatGPT is designed to reject requests for private or sensitive information about individuals. It can, however, make mistakes⁠. If ChatGPT output includes personal information about someone and they believe it is inaccurate or inappropriate, they can submit a request⁠ through the privacy request portal⁠.

A responsibility we take seriously

People are using ChatGPT in increasingly personal ways, including for questions and tasks that can touch sensitive parts of their lives. We recognize the deep responsibility that comes with that trust. We care deeply about the people who use ChatGPT, and protecting their privacy is central to how we build. We also recognize that protecting privacy and addressing serious risks of harm have to work together. We take that responsibility seriously, and we continue to strengthen how we detect and respond to credible threats of violence while maintaining privacy safeguards. You can read more about our approach to community safety and enforcement here. As our models become more capable, we will keep improving safeguards, making privacy controls clearer, and giving people practical ways to decide how their information is used.

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Comment ChatGPT apprend à connaître le monde tout en protégeant la vie privée

Un guide en langage clair au sujet de l’entraînement des modèles, les mesures de protection de la vie privée et les choix offerts en matière de confidentialité dans ChatGPT.ChatGPT est de plus en plus performant dans divers domaines et aide les gens à accomplir des tâches complexes et concrètes⁠ comme la programmation, la recherche, l’analyse et des tâches en plusieurs étapes à l’aide de différents outils. Ces gains en capacité sont rendus possibles grâce à l’entraînement sur une grande variété de données qui permettent à nos modèles d’acquérir de vastes connaissances sur le monde et de les appliquer à de nouvelles tâches.

Alors qu’OpenAI poursuit le développement de modèles de pointe, nous travaillons fort pour nous assurer que notre processus d’entraînement des modèles respecte la vie privée. Nous avons développé des technologies de pointe⁠ pour aider nos modèles à apprendre des tendances générales utiles sans apprendre d’information privée sur des individus, et nous avons mis en place plusieurs contrôles et politiques pour aider les personnes à garder le contrôle de leurs données.

Cet article explique quels…