AI Tester's Toolkit
Discover the critical role of AI testing and data annotation. You'll learn how to evaluate AI systems, identify bias, and ensure quality, making you a vital part of the AI development process.
Sessions
AI Bias Explained
After this session, you'll be able to identify common sources of AI bias and understand why it's a critical issue for AI systems.
5 min
Why AI Needs Testing
After this session, you'll be able to explain why AI isn't always perfect and why rigorous testing is essential.
5 min
Chatbot Performance Criteria
After this session, you'll be able to identify specific criteria used to evaluate a chatbot's performance, beyond just getting the 'right' answer.
5 min
Data Annotation Essentials
After this session, you'll understand what data annotation is, why clear guidelines are vital, and the importance of Inter-Annotator Agreement.
5 min
AI QA Process
After this session, you'll understand the comprehensive process of Quality Assurance (QA) for AI systems, from initial testing to continuous monitoring.
5 min
What you'll achieve
Explain why AI testing and data annotation are crucial for reliable AI systems.
Identify key evaluation criteria for chatbots, including accuracy, coherence, and safety.
Understand best practices for data labeling and the importance of Inter-Annotator Agreement (IAA).
Recognize common sources of AI bias and methods for its detection.
Describe the various stages of Quality Assurance (QA) for AI systems.
Critically assess real-world examples of AI successes and failures.
Formulate questions to identify potential flaws in AI-driven applications.