Explainable AI (XAI) — AI Technical Interview Questions & Answers

Explainable AI (XAI)

Explainable AI (XAI)

  • XAI tries to show why a model decided: feature importance, saliency on an X-ray, rule lists, counterfactuals (“if income were higher, loan yes”). Banks and hospitals need this. A 2-billion-parameter net is not self-explaining.
  • Don’t promise full transparency. Say useful hints for a human reviewer. Same topic as explainable-artificial-intelligence-xai.
  • Output: idea / do / check for Explainable AI (XAI). Fill those three in the viva.

Technical Interview Tip: One method + one high-stakes domain.

Code Examples for Explainable AI (XAI)

Example

# Explainable AI (XAI)
print("idea :", "Explainable AI (XAI)")
print("do   : one tiny example on paper")
print("check: one failure case")

Output: idea / do / check for Explainable AI (XAI). Fill those three in the viva.

How to Explain Explainable AI (XAI) to an Interviewer

Explain Explainable AI (XAI) as if you are teaching a junior — definition, then one example.

Why did the model decide? Importance / saliency / rules.

What does the example print, and what does that prove?

idea / do / check for Explainable AI (XAI)

What mistake do freshers make with Explainable AI (XAI)?

Only the buzzword, no example