Closing the AI Governance Gap: Monitoring Data Quality Drift

AI governance frameworks are evolving rapidly — but a critical gap remains.

While standards such as ISO/IEC 42001, NIST AI RMF, and the EU AI Act provide important governance principles, they still lack operational mechanisms to continuously monitor and manage data quality within AI training corpora.

Join DAMA South Africa for an insightful webinar with Dale Rutherford, who will explore this often-overlooked governance challenge and introduce practical frameworks designed to detect, measure, and remediate data quality degradation, bias amplification, and model drift in AI training data.

This session will introduce the BME (Bias, Misinformation, and Error) Metric Suite and the MIDCOT methodology, providing a structured approach to monitoring corpus health and identifying risks before they propagate into deployed AI systems.

A key theme of the discussion is AI autophagy — the self-destructive cycle where models trained on AI-generated content reinforce bias, misinformation, and declining information quality.

This session is Part 1 of a two-part series, focusing on Monitoring and Detection in AI corpus governance.

📅 Date: 16 March
⏰ Time: 16:00 (GMT+2)

If you work in data governance, AI governance, data quality, or data management, this session will provide valuable insights into how data management practices must evolve to support responsible AI.

🔗 RSVP link will be in the comments

#AI #AIGovernance #DataGovernance #DataQuality #AITrust #DAMA #DataManagement #ResponsibleAI #AISafety