Semantic Governance and Knowledge Organization in AI-Augmented Biodiversity Infrastructures

What this was about

In a pre-recorded talk, Andrew Jones argued that as AI generates annotations, classifications and links probabilistically, the key question becomes how to govern the meaning AI creates. He identified challenges of semantic ambiguity, inconsistency, provenance and institutional trust, and proposed AI-assisted rather than AI-directed curation: outputs mapped to established vocabularies, checked against community standards and accompanied by provenance, with governance built into technical architecture. He called on TDWG to define provenance-aware, semantically transparent, interoperable and community-governed practices, pointing to the Image Quality and AI Readiness Task Group.

Why it matters. Articulates governance principles for AI annotations that complement technical frameworks like DiSSCo's validation layers.

In the room

• AI can enrich records, find relationships and improve discoverability, e.g. across Tulane's millions of specimen records.
• AI can generate an answer without generating an agreed-upon meaning.
• Challenges: semantic ambiguity, inconsistency between systems or runs, provenance of machine-generated content, institutional trust.
• Existing standards and vocabularies provide shared semantic commitments that generative AI lacks.
• AI-assisted curation: map outputs to vocabularies, check against standards, record provenance, note human review.
• Recommended principles: provenance-aware, semantically transparent, interoperable, community-driven governance.

Notable moments