Government to ease data consent rules for AI development | The Asahi Shimbun Asia & Japan Watch

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

Japan Cabinet OKs easing data protection law to promote AI development

TOKYO (Kyodo) -- Japan's Cabinet on Tuesday approved a bill to revise the personal data protection law, easing restrictions on using individual inform

Bill to ease data protection law for AI development clears Japan's lower house

TOKYO (Kyodo) -- A bill to revise Japan's personal data protection law passed the House of Representatives on Tuesday, paving the way for eased restri

Spawning.ai

We believe that a future of consenting data will benefit both AI development and the people it is trained on.

Where AI Regulation Stands Today

The White House has released a National Artificial Intelligence Legislative Framework and new executive orders aiming to establish a single, nationwide standard for AI regulation...

AI and Doctrinal Collapse

Artificial intelligence runs on data. But the two legal regimes that govern data—information privacy law and copyright law—are under pressure. Formally, each re

GDPR for AI Systems: 2026 Compliance Guide (with EU AI Act Updates)

How to make AI systems GDPR-compliant in 2026 — the practitioner roadmap covering EU AI Act overlap, EDPB Opinion 28/2024, DPIAs, data minimization, sub-processor cascades, and the technical controls lawyers don't explain.

Article 78: Confidentiality | EU Artificial Intelligence Act

The Commission, market surveillance authorities and notified bodies and any other natural or legal person involved in the application of this Regulation…

Unlawful by design: Exposing the human rights costs of generative AI - Amnesty International

This briefing examines how standalone generative AI systems, based on unlawful web scraping, are in conflict with international human rights law (IHRL) and standards through their design, development and deployment. While these technologies promise sophisticated automation and efficiency, they rely on data collection and model training practices that abuse privacy rights, enable discrimination, and threaten […]

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.

DataLicenses.org

Machine-readable hints for AI agents/crawlers; easy to adopt, rely on compliance.

Data Minimisation: a Language-Based Approach (Long Version)

Data minimisation is a privacy-enhancing principle considered as one of the pillars of personal data regulations. This principle dictates that personal data collected should be no more than...

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...

Unauthorized access, personal data leaks surge in Japan | NHK WORLD-JAPAN News

A series of cyberattacks have led to leaks of large amounts of personal data from companies across Japan. Experts say the misuse of AI may be contributing to the growing threat. As this new challenge emerges, how should companies, and society as a whole, respond?