A Framework for the Systematic Review of ML Assets in AI Registries

The paper proposes a framework that adapts systematic review methods—planning, conducting, and documenting—to the retrieval of machine learning assets in AI registries. By treating models, datasets, and benchmarks as first‑class units of analysis, the framework incorporates registry‑aware search, cross‑registry schema alignment, and dependency‑driven exploration to make asset selection transparent, reproducible, and evidence‑based. The authors argue that this approach transforms ML asset discovery from an informal, ad‑hoc process into a structured, systematic practice.

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