# Rseng Fair Ml > Covers applying FAIR principles to machine learning artifacts: making models findable and reusable with model cards and rich repository metadata, documenting datasets with Croissant and datasheet-style records, licensing models and weights, linking the model-data-code-paper cluster with persistent identifiers, and the RDA FAIR4ML metadata direction. Use when a project trains, fine-tunes, publishes or reuses ML models or ML-ready datasets, when the user mentions model cards, Croissant, datasheets, FAIR4ML or model licensing, when a model heads to a hub or archive, or when evaluating whether a third-party model is documented well enough to build on. (General software FAIR is rseng-fair-software; automated repository scoring is rseng-fairguard.) - Skill: `fdiblen/rseng-fair-ml` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add fdiblen/rseng-fair-ml` - Raw SKILL.md: https://api.skillmd.com/api/skills/fdiblen/rseng-fair-ml/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - License: CC-BY-4.0 - Author: fdiblen (https://skillmd.com/u/fdiblen) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/fdiblen/rseng-fair-ml --- # FAIR for machine learning FAIR was written for data, extended to software (FAIR4RS - rseng-fair-software), and is now being worked out for machine learning (the RDA FAIR4ML interest group leads the metadata standardization). ML needs its own treatment because a model is neither data nor ordinary software: its behavior is inseparable from its training data, its "source" includes weights that no license default covers, and reuse without documentation reproduces biases invisibly. The practical FAIR-ML unit is the CLUSTER - model + data + code + evaluation + paper - linked both ways with persistent identifiers. ## Findable: metadata that machines and reviewers read - Model cards are the model's README and its FAIR metadata in one: intended use and out-of-scope uses, training data description, evaluation results with conditions, limitations and biases, licensing. Hub-hosted cards (Hugging Face's structured format) double as searchable metadata; write them from the actual training run, not memory (the experiment config and logs are the source - rseng-reproducibility). - Datasets get Croissant: the MLCommons format describes ML datasets (schema, distribution, provenance) in machine-readable form that major hubs and search index - the ML-ready complement to the generic dataset documentation in rseng-data-management. - Identifiers: archive released models and dataset snapshots with DOIs (rseng-archiving; hub storage is not preservation), and cross-link model card, dataset record, code repository (rseng-citation-metadata) and paper so each cites the others. ## Accessible and licensed honestly - State exactly what is released: weights, architecture, training code, data, all or some - and license each part explicitly. Code licenses do not fit weights cleanly; dedicated model licenses (including use-restricted ones) exist, and "weights on a hub with no license" is the ML version of unlicensed code: unusable (rseng-licensing, rseng-license-compliance for compatibility when models build on models). - Gated access is legitimate (safety, privacy, data terms) when the CONDITIONS are stated and the metadata stays public - as closed as necessary, as open as possible (rseng-regulatory-compliance where personal data trained the model; the EU AI Act documentation duties overlap helpfully with a good model card). ## Interoperable: formats and conventions - Export to exchange formats where the ecosystem has them (ONNX- class interchange for deployment; standard checkpoint formats per framework) and pin the framework versions that load the weights (rseng-reproducible-environments) - a checkpoint nobody can load is a dead artifact. - Follow the target hub's metadata conventions (tags, task taxonomy, evaluation fields) - interoperability for models is largely hub-convention compliance today, while FAIR4ML standardization matures. ## Reusable: the documentation that prevents misuse Reuse-readiness is a checklist an agent can run on any model (the user's own before release, third-party models before adoption - the rseng-software-reuse evaluation instinct): 1. Can I tell what data trained it, and under what terms? 2. Are evaluation claims reproducible - metric, dataset version, split, conditions stated (rseng-research-integrity for the numbers)? 3. Are limitations and known failure modes documented, or must I rediscover them? 4. Is the license compatible with my use, including the data's terms flowing through? 5. Can I load it - format, framework versions, hardware requirements (rseng-gpu-computing)? A "no" on any of these is a finding: fix it for your own models, weigh it for others'. Record AI/ML provenance in aidecl.yaml (rseng-ai-declaration) - a project that trains models with AI assistance has two provenance layers, and both belong in the record. ## Working with this skill This skill is source-independent: its authority is the FAIR4ML work, the Croissant specification and model-card practice linked below. It extends rseng-fair-software to ML artifacts; rseng-fairguard assesses the software side. Learn more (verified): - https://www.rd-alliance.org/groups/fair-machine-learning-fair4ml-ig/ - RDA FAIR4ML interest group - https://mlcommons.org/working-groups/data/croissant/ - Croissant ML dataset format - https://huggingface.co/docs/hub/en/model-cards - model card format and guidance - https://www.gofair.foundation/fair-principles - the FAIR principles - https://onnx.ai - ONNX model interchange format ## Related skills Check whether any of these applies before moving on: - rseng-archiving - DOIs for model and dataset snapshots - rseng-data-management - dataset documentation beneath Croissant - rseng-fair-software - the general FAIR baseline - rseng-gpu-computing - hardware requirements for model reuse - rseng-licensing - licensing weights and training data - rseng-reproducibility - model cards written from actual runs