> For the complete documentation index, see [llms.txt](https://faia.liccium.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://faia.liccium.com/consortium/whitepaper.md).

# Whitepaper

## Whitepaper

The FAIA whitepaper presents the full framework for FAIR AI Attribution: the vocabulary for disclosing AI involvement, the attribution model built on FAIA Flags, Activity Codes, and System Attribution, and the technical infrastructure for persistent, verifiable declarations bound to content through ISCC fingerprints.

It sets out the transparency problem FAIA addresses, the use cases and stakeholders it serves, and how declarations are created, signed, published, and resolved across federated registry infrastructure. It also positions FAIA in relation to related standards and initiatives, including C2PA, IPTC Digital Source Type, and the STM AI Classification.

The whitepaper is available as a pre-publication on Zenodo.

### Read the whitepaper

The FAIA Framework: A machine-readable vocabulary for AI attribution\
<https://doi.org/10.5281/zenodo.20083769>

{% embed url="<https://zenodo.org/records/20083769>" %}

### How to cite

Le Dévédec, S., Gambardella, A. A., Hettne, K., Posth, S., & Schultes, E. (2026). The FAIA Framework: A machine-readable vocabulary for AI attribution \[Whitepaper]. Zenodo. <https://doi.org/10.5281/zenodo.20083769>

The whitepaper is published under a Creative Commons Attribution-ShareAlike 4.0 International licence (CC BY-SA 4.0).

### Funding

This whitepaper is a deliverable under the Dutch Responsible AI in de praktijk programme, funded by SIDN fonds and Topsector ICT. See [Funding](/consortium/funding.md) for more about the programme and partners.


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