rseng-fair-mllisted

# 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