🤖 AI Summary
This work addresses the lack of auditable, machine-readable provenance for AI contributions in scientific research. It proposes aiprov, a general-purpose AI provenance model extending the PROV-O ontology to encompass the full human-AI collaborative workflow under FAIR principles. By embedding executable AI capabilities, the system automatically logs operations and generates metadata conforming to provenance graph constraints. Integration with ORCID identity resolution and continuous integration pipelines enforces two critical invariants: “no orphaned claims” and “only humans may authorize validation.” The project itself serves as a complete use case, demonstrating end-to-end traceability and self-auditing of AI-assisted scientific processes.
📝 Abstract
F(AI)2R is FAIR research with AI in the loop, twice: an AI-assisted authoring pass and a machine-readable audit pass over every artefact. AI systems now draft, refactor, and verify research artefacts, yet their contributions are rarely recorded in a form a later human or machine can audit. Building on the original F(AI)2R experiment, we generalize its provenance model beyond scholarly writing into aiprov, a PROV-O extension covering any AI-in-the-loop artefact, and we package the method as an executable skill that an AI agent operates itself: setup asks the human operator for their ORCID ID, resolves their identity from the public registry, and scaffolds continuous integration that gates every push on graph conformance and publishes the current build of this very paper. The paper is its own case study. Every activity, claim, and source in its production is recorded in the repository's provenance graph under two invariants: no parentless claim, and verification rungs that only humans may grant.