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 4.2: WHY EXPLAINABILITY MATTERS IN AI
The push for explainable AI stems from several crucial factors:
- Trust and Transparency: Clear explanations increase user trust in AI systems, especially when these systems are used in high-stakes areas like healthcare or criminal justice.
- Ethics and Accountability: XAI helps ensure that AI systems are fair and just, allowing stakeholders to hold models accountable for their predictions and decisions.
- Regulatory Compliance: Some regulations, such as the EU's GDPR, require AI-based decisions to be explainable, particularly when they affect individuals’ rights or personal data.