arXiv cs.AIPaper
Causal Evidentiary Governance for High-Risk Machine Learning Systems
The EU AI Act and similar regulations are real constraints now, and post-hoc explainability is failing regulators. This paper offers a concrete mechanism: commit your causal assumptions to a DAG upfront, then bind each prediction to a cryptographic proof of which paths it took. For builders deploying models in credit, hiring, or resource allocation: this is the architecture regulators will likely demand. Implementing CEG now means you're not retraining on an enforcement deadline.