AI fair metadata framework improves ecology data sharing and reuse
From FAIR to AI-FAIR Metadata-First Infrastructure for AI-Enabled Science
Artificial IntelligenceDatabasesHuman-Computer InteractionInformation Retrieval
Summary
Data used in biology and ecology are often scattered and disconnected, making it hard to use AI effectively. The authors propose new rules called AI-FAIR that help ensure data is ready for AI and that improvements made by AI go back to the original data providers. They also developed a tool that checks if datasets meet these rules and offers guidance to fix any problems. This approach makes it easier to use AI responsibly and benefits both data collectors and users.
What this means in practice
- •For data repository managers: Rate and improve datasets for AI compatibility to help AI workflows enhance ecological and biodiversity research data.
- •For machine learning engineers: Use AI-FAIR metadata criteria to evaluate and prepare training data ensuring traceability and ethical compliance in ecological AI models.
A position paper. It proposes an approach and reports no results.
Authors
Elizabeth G. Campolongo, Eric R. Sokol, Marty Downs, Gina Glenne, Gregory E. Maurer, Sydne Record, Dustin R. Rubenstein, Matthew J. Thompson, Christine Laney, Hilmar Lapp, Max Tarjan, Tanya Berger-Wolf
Abstract
Artificial intelligence (AI) technologies and their capabilities have advanced rapidly over the past few years, leading to widespread interest and adoption in the biological and ecological sciences. However, the data infrastructure and computational resources that provide the foundation for AI workflows in ecology and biodiversity science remain fragmented. Moreover, the flow of value remains largely unidirectional: data move from collectors, through repositories and aggregators into models, and finally to research products or management actions. The derived value, such as corrected taxonomies, annotations, quality flags, and insights, rarely returns to source communities. We propose an extension of the original FAIR principles to address the needs of AI-ready infrastructure: the AI-FAIR metadata criteria, which place AI-enabled science in the context of a bidirectional Question-Data-Model triangle. This framework is designed to facilitate large-scale content-based analysis across diverse collections and ensure that the scientists generating the data benefit from the AI-enabled advances built on their data. Alongside these criteria, we introduce an agentic evaluation workflow that both rates existing datasets on the AI-FAIR and traditional FAIR criteria and provides actionable feedback where critical metadata is missing or incomplete. This modular dataset evaluation tool, paired with a domain-appropriate checklist, translates these principles into a reusable assessment of machine-learning readiness, AI-readiness for specified tasks, traceability, and ethical and CARE-aligned stewardship. Through this approach we aim to create a community-informed system to close the model data loop, so that AI also enriches data to further enable scientific discovery.