Testing AI Systems and Testing with AI

This course explores how AI is revolutionizing software testing, focusing on AI-assisted testing to enhance productivity and testing AI systems to ensure fairness, robustness, and explainability. Participants will learn:

• How to integrate AI-assisted testing into workflows for greater efficiency and precision
• The distinct challenges of testing AI systems, including probabilistic outputs, model drift, and data dependencies
• Techniques to identify and mitigate issues like bias, fairness, adversarial robustness, and explainability
• Hands-on experience with AI testing tools (e.g., Google’s Fairness Indicators, SHAP, MLFlow)
• How to implement continuous monitoring and Human-in-the-Loop (HITL) validation

By the end of the course, participants will be equipped to test AI effectively and use AI to improve software testing, ensuring scalable, reliable, and ethical AI systems.