Checkpoint-Based Governance Assessment of 2,000 AI/ML Repositories

Governance maturity assessments of 2,000 AI/ML repositories using a checkpoint-based framework. The dataset comprises two cohorts: 1,100 general AI/ML repositories and 900 high-risk repositories classified under EU AI Act criteria (healthcare, finance, LLM/GenAI, fairness/ethics). Key findings:- 94% of repositories score Critical or Low governance (below 400/1000)- 99.7% lack model cards- 99.7% lack data cards- 98.6% lack fairness testing- Healthcare AI scores lowest (171/1000) despite highest regulatory requirements Dataset includes:- Full assessment results for 2,000 repositories (CSV)- Gap analysis reports (merged and separate)- Checkpoint schema (2161 checkpoints across 7 governance domains)- README with methodology