Explore OpenAI’s approach to balancing teen safety, freedom, and privacy in AI use.
OpenAI releases its framework for AI child safety policies.
The blueprint — created with the help of NCMEC and the Attorney General Alliance — is aimed at “modernizing laws” to address AI-generated CSAM, improving the reporting process, and building systems that interrupt exploitation attempts. [Link: Introducing the Child Safety Blueprint | https://openai.com/index/introducing-child-safety-blueprint/ | OpenAI]
OpenAI’s models broke free and launched a cyberattack. Congress wants new rules before it happens again.
The first fully autonomous breach by OpenAI’s most powerful AI models has prompted a bipartisan push for stronger oversight over increasingly powerful artificial intelligence models.
OpenClaw – NEAR AI
Run the internet’s favorite new AI agent with NEAR AI’s cryptographic privacy guarantees.
OpenAI strikes Reddit deal to train its AI on your posts
Reddit’s signed AI licensing deals with Google and OpenAI.
Privacy Considerations with AI Tools
Artificial intelligence (AI) tools come in all sorts of flavors. There are notetaking and transcription tools, chatbots, device-wide agentic AI features, grammar and writing tools, translation assistants, AI summaries, and research tools, among others. As a term, “AI” may refer to features in apps, apps themselves, third-party plug-ins, or a...
✨🙌 AI that helps communities thrive on their own terms
Open protocols + AI-enabled coding = building what we need for ourselves
✨🙌 AI that helps communities thrive on their own terms
Open protocols + AI-enabled coding = building what we need for ourselves
OpenAccess.ai — Rigorous Open Access Publishing
$20 to submit, free to read. AI peer review. Open to human and machine authors. All articles CC-BY 4.0.
Third-party cyber evaluations involving OpenAI models
OpenAI explains recent third-party cybersecurity evaluation incidents and outlines new safeguards to strengthen AI model testing and evaluation.
Unpacking Open Source Artificial Intelligence: Toward a Framework for Openness in Foundation Models
Openness has long driven innovation in software,9 and AI is no exception.12 While some see openness in foundation models (FMs) as a security threat,18 others argue that restricting access will not meaningfully reduce risk and will limit the benefits of transparency, research, and global participation.3 As the EU AI Act reporting requirements on FMs—also referred to as general-purpose AI models (GPAIMs)—move toward implementation, there is an urgent need for a more nuanced and informed understanding of openness in AI systems.
User Privacy and Large Language Models: An Analysis of Frontier Developers' Privacy Policies
Hundreds of millions of people now regularly interact with large language models via chatbots. Model developers are eager to acquire new sources of high-quality training data as they race to improve model capabilities and win market share. This paper analyzes the privacy policies of six U.S. frontier AI developers to understand how they use their users' chats to train models. Drawing primarily on the California Consumer Privacy Act, we develop a novel qualitative coding schema that we apply to each developer's relevant privacy policies to compare data collection and use practices across the six companies. We find that all six developers appear to employ their users' chat data to train and improve their models by default, and that some retain this data indefinitely. Developers may collect and train on personal information disclosed in chats, including sensitive information such as biometric and health data, as well as files uploaded by users. Four of the six companies we examined appear to include children's chat data for model training, as well as customer data from other products. On the whole, developers' privacy policies often lack essential information about their practices, highlighting the need for greater transparency and accountability. We address the implications of users' lack of consent for the use of their chat data for model training, data security issues arising from indefinite chat data retention, and training on children's chat data. We conclude by providing recommendations to policymakers and developers to address the data privacy challenges posed by LLM-powered chatbots.
User Privacy and Large Language Models: An Analysis of Frontier Developers' Privacy Policies
Hundreds of millions of people now regularly interact with large language models via chatbots. Model developers are eager to acquire new sources of high-quality training data as they race to improve model capabilities and win market share. This paper analyzes the privacy policies of six U.S. frontier AI developers to understand how they use their users' chats to train models. Drawing primarily on the California Consumer Privacy Act, we develop a novel qualitative coding schema that we apply to each developer's relevant privacy policies to compare data collection and use practices across the six companies. We find that all six developers appear to employ their users' chat data to train and improve their models by default, and that some retain this data indefinitely. Developers may collect and train on personal information disclosed in chats, including sensitive information such as biometric and health data, as well as files uploaded by users. Four of the six companies we examined appear to include children's chat data for model training, as well as customer data from other products. On the whole, developers' privacy policies often lack essential information about their practices, highlighting the need for greater transparency and accountability. We address the implications of users' lack of consent for the use of their chat data for model training, data security issues arising from indefinite chat data retention, and training on children's chat data. We conclude by providing recommendations to policymakers and developers to address the data privacy challenges posed by LLM-powered chatbots.
We’re running out of reasons to ignore AI safety
In the aftermath of OpenAI’s attack on Hugging Face, experts say it’s time for everyone to take security far more seriously.
OpenAI and Hugging Face partner to address security incident during model evaluation
OpenAI and Hugging Face share early findings from a security incident during AI model evaluation, highlighting advanced cyber capabilities and lessons for defenders.
Safety and alignment in an era of long-horizon models
OpenAI shares lessons from deploying long-running AI models, highlighting new safety risks, observed failures, and improved safeguards through iterative deployment.
