FAQ: Common Questions About Explainable AI

Intermediate

❓ 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