
This white paper describes the FAIA - FAIR AI Attribution Framework. FAIA provides a vocabulary and technical framework for machine-readable, persistent, and verifiable disclosure of AI involvement. 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 declarations can be bound to ISCC fingerprints, allowing attribution information to remain resolvable even when files are copied, transformed, or stripped of metadata. FAIA supports consistent transparency across sectors including publishing, journalism, research, and media production. It supports compliance with emerging obligations such as the EU AI Act and provides a foundation for services that depend on reliable provenance information, including content verification, moderation, search, and training data curation.
This white paper is a deliverable under the Dutch “Responsible AI in de Praktijk” programme, funded by SIDN fonds and Topsector ICT.
FAIR data, Artificial Intelligence/standards
FAIR data, Artificial Intelligence/standards
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
