FAQ: Common Questions About Explainable AI
❓ Frequently Asked Questions: Explainable AI (XAI)
Q1: Why is explainability important in AI?
🔍 A1: Explainability builds trust, ensures transparency, and enables validation of AI decisions—especially critical in fields like healthcare and finance.
Q2: Can all models be made explainable?
⚙️ A2: Not all.
- Complex models like deep neural networks often need post-hoc explanations
- Simpler models are inherently interpretable
Q3: Are explanations always accurate?
📏 A3: Not necessarily.
- Explanation fidelity varies
- Some techniques may approximate or simplify the true decision process
Q4: How do I choose the right XAI method?
🎯 A4: Consider:
- 📐 Model complexity
- 🔍 Local vs. global explanation needs
- ⚙️ Computational resources
- 🧑 Target audience
Q5: What are the limitations of XAI?
⚠️ A5: Limitations include:
- ✂️ Potential oversimplification
- 🖥️ Increased computational cost
- 🧩 Difficulty with high-dimensional data
✅ Takeaway
Understanding these aspects is essential for deploying AI systems that are:
- 🤝 Effective
- 📖 Responsible
- 🔒 Trustworthy