Module 4: Explainable AI (XAI)
View
Understand the importance of explainability in AI and its role in building ethical AI systems.
In Module 4, we cover:
Lesson 4.1: Introduction to Explainable AI
Lesson 4.2: Why Explainability Matters in AI
Lesson 4.3.: XAI Tools and Methods
Lesson 4.4.: Practical Steps for Implementing XAI
Lesson 4.5: Challenges and Future Directions in XAI Implementation
LESSON 4.4.: PRACTICAL STEPS FOR IMPLEMENTING XAI
To incorporate XAI into AI projects, practitioners should:
- Identify Stakeholder Needs: Determine who will use the model explanations and what level of detail they require.
- Select Appropriate XAI Tools: Choose XAI techniques and tools that best suit the model type and application context.
- Implement and Test: Use tools like LIME, SHAP, or the What-If Tool to test model predictions and identify any biased patterns.
- Communicate Results Effectively: Tailor explanations to the audience—technical explanations may be appropriate for data scientists, while simplified summaries may work better for end-users or non-technical stakeholders.