This dataset models safety erosion cascades driven by cost pressure in AI operations. It detects when cost pressure rises, safety buffers weaken, governance lag grows due to thin staffing and delayed review, and tight coupling through shared pipelines and automation crosses the five-node cascade threshold into an unrecoverable safety erosion cascade.
This dataset models a five-node cascade: four interacting instability drivers and one emergent cascade state.The… See the full description on the dataset page:
https://huggingface.co/datasets/ClarusC64/ai-5node-cost-buf-lag-cpl-cost-cut-safety-erosion-v0.1.
