Understanding the source of what we see and hear online

Advancing content provenance for a safer, more transparent AI ecosystem

OpenAI advances AI content provenance with Content Credentials, SynthID, and a verification tool to help people identify and trust AI-generated media.

Making it easier to understand how content was created and edited

We're expanding our tools to help you understand how content was created and edited across the web.

Can temporal article-level credibility signals improve domain-level credibility prediction?

Web domain credibility evaluation is vital for combating misinformation. It is conducted by examining factors such as domain type, transparency, and overall reputation.

Graphical Models of False Information and Fact Checking Ecosystems

arXiv:2208. 11582v2 Announce Type: replace-cross Abstract: The wide spread of false information online, including misinformation and disinformation, has become a major problem for our highly digitised and globalised society.

Beyond "Made with AI": Visualizing Provenance Density to Mitigate the Transparency Penalty

The paper introduces Provenance Density, an interface that visualizes the density of verified claims within a text to counter the Fluency Trap—where users mistake fluent AI-generated hallucinations for truth. In a study with 81 participants, the interface significantly improved users’ ability to distinguish true from fabricated content, while no signal led to no discernment. A technical audit of 200 samples revealed that retrieval density alone is insufficient, and that the Consistency Veto provides most of the discriminative power for dynamic queries.

SynthID Detector — a new portal to help identify AI-generated content

Learn about the new SynthID Detector portal we announced at I/O to help people understand how the content they see online was generated.

Announcing our new Content Guidelines and Policy

Epstein Files Engine: Agentic Search for Investigative Journalism

The Epstein Files Engine is an AI agent developed by the New York Times to help journalists investigate a massive mixed‑media collection released by the U.S. Department of Justice on January 30, 2026, which contains about three million pages of PDFs related to Jeffrey Epstein. The Engine translates reporter questions into Google BigQuery SQL queries across three corpora—Epstein‑related releases, the Times’s archive, and external Epstein‑related news headlines—using an LLM to plan queries and return citation‑rich answers that reporters can verify. Over 100 journalists used the Engine, contributing to at least 20 published stories, and the system includes a Diff method for text‑and‑visual duplicate matching to surface genuinely new information.

EvidenT: Building Trustworthy Enterprise Assistants through Evidence Groundedness and Traceability

arXiv:2609.22537v1 Announce Type: new Abstract: Enterprise AI assistants must produce responses that are verifiable and traceable to source evidence. However, retrieval augmented generation (RAG) ove...

Supporting Europe’s work in ensuring a trustworthy AI ecosystem

OpenAI supports the EU Code of Practice on AI content transparency, advancing provenance standards and tools to help people understand AI-generated content.

Profiling and Evolution of Intellectual Property

arXiv:2204. 09333v3 Announce Type: replace-cross Abstract: In recent years, with the rapid growth of Internet data, the number and types of scientific and technological resources are also rapidly expanding.

ProvenAI: Provenance-Native Traces of Evidence in Generated Answers

arXiv:2606. 26449v1 Announce Type: cross Abstract: Retrieval-augmented systems routinely present citations alongside generated answers, yet a citation does not confirm that the corresponding source meaningfully shaped the output.