Human work deserves measurable recognition.
Why does attribution matter in generative AI?
Attribution connects an AI answer to the work behind it and measures each source’s contribution. Publishers can inspect how their content is used, and organizations can use that evidence to inform compensation.
Why does source transparency matter in an AI world?
A healthy information ecosystem depends on trust between readers, creators, enterprises, and generative AI platforms. When an answer brings together information from many sources, those contributions need to be visible.
Attribution connects claims to the evidence behind them. Readers can examine the sources, creators can understand how their work is used, and organizations can review the content they share.
Consistent, auditable contribution records provide a shared basis for recognition and compensation, helping sustain the human knowledge that makes useful AI possible.
Output-based attribution
Measuring the contribution of sources by examining the generated answer.
What does a citation leave out?
A citation points readers to a source. Attribution examines the claims that source supports and measures its contribution to the answer.
A source was mentioned.
A link acknowledges a source. It does not quantify that source’s contribution to individual claims.
A contribution was measured.
Claims are matched to source evidence, and each source’s contribution is displayed as an inspectable share of the answer.
“Transparency that cannot be inspected is not transparency.”ProRata, “Citations Aren’t Enough”, January 2026
Revenue risk
Citation frequency is not a measure of contribution. Using it as a payment basis can confuse being named with supplying the substance of an answer.
Brand risk
A cited publication can appear to support a claim it never made. Claim-level verification distinguishes evidence from association.
Build trust
Enable output-based compensation
Support quality content
Protect brand identity
Promote transparency
How can measurement support compensation?
Contribution scores provide a basis for payout calculations. Commercial agreements determine the terms. The goal is to connect use of human work to the resources needed to keep creating it.
Credit keeps
creation moving.
- 01
Quality content
Original work gives AI something worth answering with.
- 02
Better AI
Useful answers depend on human knowledge.
- 03
Attribution and credit
Contribution becomes measurable and inspectable.
- 04
Compensation
Payment can follow measured contribution.
- 05
More original work
Revenue helps fund the next generation of ideas.
Other media sectors offer a precedent for measured use and payment. Streaming, video revenue-sharing, and news subscriptions connect recognized rights with a basis for distributing value. The mechanisms differ; the principle is that use should be measured and paid for.
The standards adopted now will shape future content economics. Publishers, developers, and policymakers should ask for a claims-based standard that can be inspected and challenged.
Without Attribution,
Quality Content Cannot Survive
The ProRata white paper explores the method and the economics behind output-based attribution.
Read the white paperSee how an answer connects to its sources.
Studies have shown that educated individuals tend to live longer, healthier lifestyles.
Attribution by ProRataInteractive example
FAQ
How is attribution different from a citation?
A citation points to a source. Attribution measures how much that source contributed to an answer, with evidence that can be inspected at the claim level.
Does attribution determine what someone is owed?
No. Attribution measures contribution. Contracts, rights, and commercial arrangements determine compensation.
Does attribution replace input-side telemetry?
No. Input telemetry records what was retrieved. Output-based attribution examines what contributed to the answer, and by how much.