Feature Request: EU AI Act compliance mapping for data quality checks

Summary

With the EU AI Act enforcement deadline on August 2, 2026, data quality tools like Cleanlab become critical for compliance. Article 10 (Data Governance) specifically requires that training data meets quality criteria, and Cleanlab's data-centric approach directly addresses this.

What this could look like

• Art. 10 (Data Governance): Map Cleanlab's data quality checks (label errors, outliers, duplicates) to Article 10 requirements, generate compliance-ready reports
• • Art. 11 (Documentation): Auto-generate data quality documentation that satisfies Article 11's technical documentation requirements
• • • Art. 9 (Risk Management): Flag datasets with high error rates as compliance risks

Context

I ran Cleanlab through AIR Blackbox, an open-source EU AI Act compliance scanner (Apache 2.0). You can run it yourselves:

pip install air-blackbox
air-blackbox comply --scan . --no-llm --format table --verbose

Everything runs locally, no data leaves your machine.

Why this matters

The EU AI Act's data governance requirements (Article 10) are some of the most specific and actionable in the legislation. Cleanlab already solves the hard problem of finding data quality issues. Adding explicit EU AI Act mapping turns every Cleanlab report into compliance evidence, which is a strong differentiator for enterprise adoption.