# How to find trusted datasets for AI

There is no shortage of datasets. There is a shortage of datasets you can put in production and defend when someone asks where a number came from. The gap between the two is provenance.

Start with discovery. Google Dataset Search indexes datasets that publish structured metadata, so a well-described dataset is findable by subject, source, and update cadence. Registries and catalogs list more. The catalog is the easy part.

## The hard part is qualification

For each candidate, ask who publishes it and whether they sign it. A named publisher with signed records is accountable; an anonymous dump is not.

What is the original source? A dataset derived from a primary source should say which one and link it. "Compiled from various sources" is a flag.

How fresh is it, and how do I know? A dataset should carry the time of its most recent read, not a vague "updated regularly."

Can I verify a row? The strongest datasets let you check a single record's signature against a published key, so you are not trusting the whole file to trust one value.

What can I legally do with it? Discovery turns up plenty of data whose terms forbid commercial use. Read the license before you build.

## Source it, do not just collect it

A dataset that answers these is one you can hand to a reviewer without a follow-up meeting. A dataset that does not is a bet you are making quietly.

The shift worth making is from collecting data to sourcing it: treating each dataset the way a desk treats a source, with a name, a time, and a way to check. It is slower at the front and far faster at the back, when the questions come.

DRM3 keeps a catalog of signed datasets built on this model. Each one shows its source, the time it was read, and a receipt you can check: [find trusted datasets for AI](https://drm3.io/datasets).

*Originally published on* [*drm3.io*](https://drm3.io/blog/how-to-find-trusted-datasets-for-ai)*.*