Building privacy into Kin, and personal AI — Kin

Join the Kin Personal AI Discord Server!

The official Discord server for Kin, a personal AI focused on protecting your private data. Join to chat to the devs directly, and meet others around the world who are interested in the forefront of e

HUGE — AI Without Giving Up Your Privacy

We're building an Agentic AI Platform running on your Device that lives with YOU, isolated from the cloud, fundamentally reimagining the relationship between humans and artificial intelligence through ownership, privacy, and massive context. In an age of capture and control, HUGE sells independence and sovereignty.

Teen safety, freedom, and privacy

Explore OpenAI’s approach to balancing teen safety, freedom, and privacy in AI use.

Data privacy concerns in AI companion apps - Surfshark

As AI grows, digital companions help with loneliness but raise questions about user data privacy. Many seek virtual relationships, but it's important to remember they are business-driven, not personal bonds.

Top 4 AI chatbot privacy concerns and how to mitigate them | TechTa...

Explore four key chatbot privacy concerns, as well as privacy protection strategies for individual users and organizations hoping to safeguard user data.

Privacy Policy

Anthropic is an AI safety and research company that's working to build reliable, interpretable, and steerable AI systems.

Japan relaxes privacy laws to make AI development easy

: Opting out of personal data use won't be an option because Minister says that's a 'very big obstacle' to AI adoption

AI tools can unmask anonymous accounts

Goodbye, privacy.

OpenClaw – NEAR AI

Run the internet’s favorite new AI agent with NEAR AI’s cryptographic privacy guarantees.

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.

Meeting where AI eyes can't follow

I applied to the Community Privacy Residency, and the past projects reminded me of a cryptographic exploration I come back to every once in a while, just for...

Signal President Meredith Whittaker calls out agentic AI as having 'profound' security and privacy issues | TechCrunch

Signal President Meredith Whittaker warned Friday that agentic AI could come with a risk to user privacy. Speaking onstage at the SXSW conference in

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 Fiction in the Wild

This website hosts anonymized ChatGPT-user conversations where users requested some form of fiction generation—including stories, novels, scripts, roleplay, hypothetical scenarios, erotic imaginings, and more. The data is drawn from WildChat and was collected voluntarily and with users’ consent between 2023 and 2024. The models were powered by GPT-3.5 and GPT-4.