Global Data Pledge

We, the undersigned, commit to making the publicly accessible data our institutions hold available for AI training — and to doing so under terms that ensure the value our contribution creates flows back to the communities that produced it.

We recognize that our holdings — government records, public broadcasting archives, library collections, scientific publications, public-domain texts, openly licensed creative works — are part of the substrate from which AI is built. We commit to making them accessible through formats that enable lawful AI training, described in a shared vocabulary, and recorded in the AI Commons Registry so that downstream use is traceable, auditable, and reciprocal.

The data winter is real. The thaw requires commitment.

Researchers have documented a decline in what's been called the AI Data Commons — the corpus of openly accessible material that public-interest, research, and open-model AI development depends on. Web scraping under contested legal conditions has produced a backlash; publishers are walling off content; opt-outs are accelerating. The current trajectory will leave open-data AI development structurally disadvantaged against the closed-data frontier.

The data winter is not an inevitability. It is a coordination failure. Most of the institutions holding high-value data want their work to power public-interest AI. What's missing is the framework that lets them contribute without contributing to extraction.

The Data Pledge provides that framework. Availability without reciprocity is extraction. Reciprocity without availability is performance. Together, coordinated through a shared vocabulary and the Registry, they are how a public-interest data commons gets built at the scale the moment requires.