In this week's episode, Matthias Reinwarth and Alexei Balaganski discuss the growing importance of AI Data Provenance. The conversation explores why provenance is distinct from traditional logging, the operational gaps between ML engineering practices and regulatory expectations, and the regulatory context driving these requirements.

They get into the risks of attempting to retrofit governance after AI systems are already deployed and explain why provenance must be built directly into data and model workflows.

 Key Topics Covered:
✅ AI data provenance is a new and urgent issue.
✅ Low-quality data leads to poor AI outcomes.
✅ Auditing and compliance are essential for AI systems.
✅ Organizations must establish governance for AI data.
✅ Data catalogs and traceability are foundational.
✅ Prepare for AI regulations like GDPR.
✅ Start small and apply a risk-based approach.
✅ Never trust, always verify your data sources.