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

# FAIA – Fair AI Attribution

## The FAIA Project

The **FAIA project** (Fair AI Attribution) develops an open, structured framework to disclose the role of artificial intelligence in content creation. FAIA is developed by [**Liccium**](https://liccium.com/) in collaboration with [**Leiden University**](https://www.universiteitleiden.nl/en) and the [**GO FAIR Foundation**](https://www.gofair.foundation/), addressing a growing need for transparency in digital publishing and research. 

As AI tools become more common in writing, publishing, and media production, transparency is no longer optional - it’s essential for trust, credibility, and compliance. With the increasing integration of generative AI tools – ranging from writing assistants to full content generators – creators, publishers, and academic researchers need a consistent and verifiable way to indicate whether and how AI has contributed to the content. FAIA provides that mechanism.

The framework is implemented as a **Liccium plugin**, allowing users to flag AI involvement directly within Liccium's declaration and signing interface. These flags are machine-readable, interoperable, and persistently linked to the content.

## The FAIA Homepage and Explorer

More information you will find on: [https://faia.liccium.com](https://faia.liccium.com/)

The FAIA Registry Exlorer: 

{% embed url="<https://faia.io>" %}


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