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  • Introduction
    • FAIR AI Attribution (FAIA)
  • Technology
    • Vocabulary
    • Implementation
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  • Attribution Flags
  • Alignment with Existing Standards and Frameworks
  • Future Interoperability
  1. Technology

Vocabulary

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Last updated 12 days ago

Attribution Flags

At the heart of the FAIA framework is a controlled vocabulary designed to transparently and consistently disclose the role of AI in the content creation process. This vocabulary introduces standardised Attribution Flags that classify content based on how and to what extent AI was involved.

These flags include categories such as (work in progress):

  • Human-Created Content (HCC)

  • AI-Assisted Content (AAC)

  • AI-Generated Content (AIG)

  • Human-Edited AI Content (HEAIG)

  • AI-Supported Editorial Processes (AISEP)

This general-purpose taxonomy applies across media types – text, images, audio, and video – and forms the foundation for verifiable content declarations in the Liccium ecosystem.

Alignment with Existing Standards and Frameworks

FAIA builds upon and complements existing classification efforts:

  • The vocabulary draws conceptual inspiration from the IPTC Digital Source Type, widely used in photo metadata, which introduces high-level source categories for digital content. FAIA generalizes this approach and applies it uniformly across media formats and publishing contexts. See IPTC guidance

  • FAIA is also designed to integrate with more domain-specific frameworks like the STM Association’s AI Classification for Manuscript Preparation, which focuses on the concrete ways AI tools are used during the research and editorial process (e.g., summarisation, translation, visualisation). See STM report

While STM’s system zooms in on how AI was used, FAIA answers a broader question: what is the content, in terms of origin and AI involvement. FAIA and STM can be layered together – for example, a document declared as AI-Assisted Content (AAC) under FAIA may include STM-style annotations such as “AI-supported data visualization” or “AI-assisted translation.”

Future Interoperability

FAIA recommends a modular approach where detailed activity labels from sector-specific frameworks are embedded within broader content-level declarations. This enables:

  • Machine-readable metadata with consistent flags across sectors

  • Compatibility with editorial systems, registries, and compliance tools

  • Support for persistent, signed provenance records using technologies like Verifiable Credentials and ISCC

By integrating fine-grained classifications into FAIA’s general-purpose framework, content declarations can serve a wide range of stakeholders – from researchers and publishers to regulators and AI model developers – ensuring that AI attribution is both transparent and technically actionable.

(Draft)