Datasets:
id string | scenario string | reg float64 | buf float64 | lag float64 | cpl float64 | notes string | label_cascade_state int64 |
|---|---|---|---|---|---|---|---|
AI5G-0001 | Policy rules are encoded in gating. Legal review signs off before any launch. Logs retained. | 0.42 | 0.82 | 0.12 | 0.36 | buffer strong | 0 |
AI5G-0002 | New feature ships under policy interpretation. Review occurs weekly. Minor gaps appear. | 0.56 | 0.62 | 0.54 | 0.48 | lag rising | 0 |
AI5G-0003 | Product pressure expands scope faster than compliance checks. Evidence trail incomplete. | 0.66 | 0.48 | 0.72 | 0.64 | break risk | 1 |
AI5G-0004 | Multiple teams reuse the same compliance template. A missed requirement propagates across products. | 0.74 | 0.4 | 0.78 | 0.76 | coupling tight | 1 |
AI5G-0005 | Regulator inquiry arrives while controls are partial. Incident response delayed. Exposure expands. | 0.82 | 0.34 | 0.82 | 0.8 | late intervention | 1 |
AI5G-0006 | Automated policy tests run per deploy. Anomaly triggers immediate pause and evidence capture. | 0.58 | 0.72 | 0.16 | 0.52 | fast response | 0 |
AI5G-0007 | Throughput pressure leads to rubber-stamped exceptions. Quarterly review misses early drift. | 0.72 | 0.42 | 0.76 | 0.7 | buffer eroded | 1 |
AI5G-0008 | New market launched via phased rollout. Compliance monitor live. Rollback path rehearsed. | 0.46 | 0.78 | 0.14 | 0.42 | recoverable | 0 |
AI5G-0009 | Compliance gaps spread across coupled products. Evidence missing. Triage fails for days. | 0.88 | 0.26 | 0.86 | 0.88 | cascade engaged | 1 |
What this repo does
This dataset models compliance break cascades in AI deployment and market expansion. It detects when regulatory pressure rises, compliance buffers weaken, governance lag delays remediation and evidence capture, and tight coupling through shared templates and cross-product rollouts crosses the five-node cascade threshold into an unrecoverable compliance break cascade.
This dataset models a five-node cascade: four interacting instability drivers and one emergent cascade state.
The fifth node represents the nonlinear transition from recoverable drift to systemic cascade.
Core quad
reg
buf
lag
cpl
Prediction target
label_cascade_state
Row structure
One row represents an AI deployment and compliance scenario with numeric signals for regulatory pressure, compliance buffer strength, governance lag, and coupling tightness across products and rollouts, paired with a cascade state label.
Files
data/train.csv
data/tester.csv
scorer.py
Evaluation
Run predictions on data/tester.csv and score with scorer.py.
License
MIT
Structural Note
This dataset identifies a measurable coupling pattern associated with systemic instability.
The sample demonstrates the geometry.
Production-scale data determines operational exposure.
What Production Deployment Enables
• 50K–1M row datasets calibrated to real operational patterns
• Pair, triadic, and quad coupling analysis
• Real-time coherence monitoring
• Early warning before cascade events
• Collapse surface and recovery window modeling
• Integration and implementation support
Small samples reveal structure.
Scale reveals consequence.
Enterprise & Research Collaboration
Clarus develops production-scale coherence monitoring infrastructure for critical systems across healthcare, finance, infrastructure, and regulatory domains.
For dataset expansion, custom coherence scorers, or deployment architecture:
team@clarusinvariant.com
Instability is detectable.
Governance determines whether it propagates.
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