Chapter A · 8 pages · ~116 min
Data fuels AI
Understand how examples, labels, coverage, quality, and feedback shape AI behavior, and how data choices create privacy, bias, and governance responsibilities.
What you will be able to do
- Explain how training examples and labels shape learned patterns
- Evaluate data quality, coverage, provenance, and fitness for a specific purpose
- Recognize sampling, historical, measurement, and labeling bias
- Distinguish training, evaluation, inference, and feedback data
- Create a responsible data plan with consent, documentation, monitoring, and human oversight