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FAIA (FAIR AI Attribution) is an open framework for structured, machine-readable, and verifiable disclosure of AI involvement in digital content. This page provides a short project description, logos, overview graphics, and a media contact for journalists, researchers, and partners.
Media contact
For interviews, background, or asset requests, contact info@faia.io.
About FAIA
FAIA provides a shared vocabulary and technical model for disclosing whether and how AI systems contributed to the creation or modification of digital content. It defines three complementary elements: high-level attribution flags (human-created, AI-assisted, AI-generated), activity codes describing the role AI played in the content lifecycle, and optional system attribution identifying the AI system and version involved.
FAIA supports disclosure at different levels. At the lightweight end, the FAIA statement generator (https://www.faia.io/statement-generator) lets creators and organisations produce portable transparency statements that are both human-readable and machine-readable, and share them through a URL, embed them on a website, or attach them to a publication, with no specialised infrastructure required. At the more advanced end, declarations can be issued and cryptographically signed by creators, rightsholders, institutions, or AI system providers, expressed as machine-readable JSON-LD, and validated through a trust model based on digital certificates and verifiable credentials.
Persistent declarations are bound to content through ISCC fingerprints (ISO 24138:2024) and published across a federated registry, so attribution remains resolvable even when files are copied, transformed, or stripped of metadata. FAIA supports transparency across publishing, journalism, research, and media production, and provides a foundation for compliance with emerging obligations such as the EU AI Act.
FAIA is developed by Liccium in collaboration with Leiden University and the GO FAIR Foundation.
Short description
FAIA (FAIR AI Attribution) is an open, machine-readable framework for verifiable disclosure of how AI was involved in creating or modifying digital content. It is developed by Liccium with Leiden University and the GO FAIR Foundation, supported by SIDN fonds and Topsector ICT.
Logos
The graphics are provided for editorial and informational use referring to the FAIA project and its partners. They are published under a Creative Commons Attribution-NoDerivatives 4.0 International licence (CC BY-ND 4.0), so they may be reproduced and shared unaltered, with attribution to FAIA (FAIR AI Attribution), but may not be modified, recoloured, cropped, distorted, rotated, or otherwise adapted. Please keep adequate clear space around them. Source files are available on request, and for other uses contact info@faia.io.
FAIA Flags
Overview graphics
These graphics may be reproduced unaltered in articles, presentations, and reports about FAIA, with attribution to FAIA (FAIR AI Attribution). Source files are available on request.
Project partners
Leiden University (project lead) provided academic oversight and developed the ethical, legal, and scholarly foundations of the framework, with a focus on transparency, reproducibility, and research integrity. Contributors include Leiden University Libraries, Centre for Digital Scholarship.
The GO FAIR Foundation contributed expertise in semantic data modelling, metadata standards, and FAIR principles, helping ensure that the FAIA vocabulary and declarations are interoperable, machine-readable, and openly accessible.
Liccium developed the technical infrastructure, the applications, and provides a reference implementation of the FAIA framework within its content verification software.
Funding
FAIA is a deliverable under the Dutch Responsible AI in de praktijk programme, a joint funding initiative by SIDN fonds and Topsector ICT supporting practical applications of trustworthy AI. See Funding for more.
Resources
Whitepaper on Zenodo (pre-publication): https://doi.org/10.5281/zenodo.20083769
FAIA registry: https://faia.io
FAIA statement generator: https://www.faia.io/statement-generator
Documentation: https://faia.liccium.com
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