AAIA Course #20: Audit Data Quality and Data Analytics

Master data-quality assessment and audit analytics techniques for AI systems. This video covers completeness, accuracy, representativeness, and timeliness checks—plus sentiment analysis, source-to-score reconciliation, and full-population testing to uncover hidden data problems that sample-based audits miss. You'll learn to distinguish between assessing an AI system's data quality and using analytics as an audit technique. Both are critical: poor audit data invalidates your fairness conclusions, and sample-based testing can hide systematic errors that affect entire populations. Key Topics Covered: • Four dimensions of data quality: completeness, accuracy, representativeness, timeliness • Sentiment analysis for unstructured data and rubric validation • Source-to-score reconciliation testing full populations, not just approvals • Anomaly detection and Benford's Law for pattern-breaking records • Population-scale testing vs. sampling: when to use each • Visualization and source labeling to communicate findings accurately CHAPTERS 0:00 Introduction 0:47 Key Concepts and Foundation 2:28 Two Jobs in One Topic 3:35 Four Dimensions of Data Quality 7:42 Audit Analytics Toolkit 9:37 Source-to-Score Reconciliation 12:10 Full-Population Testing 15:13 Visualization and Data Labeling 16:26 Decision Rules and Takeaways 16:58 Practice Quiz Questions RESOURCES 🔗 ISACA AAIA Certification: https://www.isaca.org/credentialing/aaia 📋 Full Playlist: https://www.youtube.com/@StackLessons... ABOUT StackLessons creates hands-on exam prep content for cloud & AI certifications. Like & Subscribe for more content! PRACTICE QUESTIONS Need more practice questions? Visit https://certcompanion.com/exams #ISACA #AAIA #AIAudit 📌 Chapters 0:00 Introduction 0:47 Key Concepts and Foundation 2:28 Two Jobs in One Topic 3:35 Four Dimensions of Data Quality 7:42 Audit Analytics Toolkit 9:37 Source-to-Score Reconciliation 12:10 Full-Population Testing 15:13 Visualization and Data Labeling 16:26 Decision Rules Recap #ISACA #AAIA #DataQuality