Validating and Enhancing AI Features

This unit teaches methods for validating AI/ML product changes, balancing model generalization, and integrating human expertise into AI-enabled features. Students will practice designing experiments, diagnosing model behavior, planning feature enhancements, and assessing operational and ethical trade-offs that arise in real product contexts.

Learning Objectives

• Analyze and design A/B tests for AI/ML product changes, including selecting metrics, calculating sample sizes, and interpreting statistical outcomes to determine causal impact
• Apply diagnostic techniques to identify overfitting and underfitting, quantify bias-variance trade-offs, and select appropriate mitigation strategies (e.g., regularization, cross-validation, data augmentation)
• Demonstrate the design of human-in-the-loop workflows by defining intervention points, escalation paths, quality-monitoring metrics, and documentation for human-AI collaboration
• Evaluate feature-enhancement proposals and system-level challenges (latency, fairness, data drift, maintenance) and produce a prioritized mitigation plan that balances business impact, technical risk, and user experience