XAI Interview Experience | From Model Interpretability to Real Interview Insights

Recently, many candidates preparing for AI/ML roles have encountered the keyword “XAI” (Explainable AI). In many companies today, it’s no longer a “nice-to-have” — it’s a must-have skill.

Here’s a detailed recap of my full interview process for an XAI-related position (Machine Learning Engineer / Applied Scientist). Hopefully, this can help those preparing for similar roles.

Role Background & Interview Process

The position was Machine Learning Engineer at a FinTech company, focusing on model interpretability for credit risk models.

The entire process consisted of four rounds:

Recruiter Phone Screen – Resume walkthrough, project highlights, and motivation for XAI.
Technical OA (Online Assessment) – One modeling question + one model interpretation problem.
Technical Interview – In-depth discussion on interpretability methods and business understanding.
Hiring Manager Round – Project deployment and teamwork discussions.

OA & Technical Questions Review

Question 1: Model Interpretation in Credit Scoring

Prompt:

Given a trained XGBoost model predicting credit default probability, how would you explain the top features influencing the prediction for a given user?

Key concepts tested:

Familiarity with SHAP / LIME / Feature Importance
Understanding the difference between local and global explanations
Ability to explain the business meaning behind features (e.g., higher “income-to-loan ratio” → lower default probability)

My approach:

Used shap.TreeExplainer(model) to generate explanations
Visualized single-sample contributions via shap.force_plot()
Interpreted top features in context with credit risk logic

Follow-up question:

“What if the model is a deep neural network?”

I extended the discussion to Gradient SHAP and Integrated Gradients for deep models.

Question 2: Debug a Misleading Interpretation

Prompt:

A model shows that "number of previous loans" is the most important feature, but in reality, it’s due to data leakage. How would you detect and fix it?

This was a trap question testing whether the candidate truly understands that interpretability ≠ causality.

Key points:

Check if the candidate recognizes potential data leakage
Validate feature generation logic and data independence
Verify with time-based validation and permutation importance
Use PDP / ICE plots to double-check feature effects

The interviewer appreciated that I treated interpretability as a scientific analysis process, not just a visualization trick.

System Design & Discussion Round

In the final round, I was asked to design an explainability system for a credit risk model:

“If you were to build a model interpretability platform for a risk system, how would you design its architecture?”

My response included three layers:

Data Layer: Raw data + feature engineering intermediates
Model Layer: Model versioning and prediction logs
Explanation Layer: SHAP/LIME computation service + visualization dashboard

I also mentioned evaluation metrics such as fidelity, stability, and consistency, and how to validate the system through A/B testing.

Key Project Deep-Dive Topics

During project discussions, the interviewer focused on:

How business users utilized model explanations
Whether explanations actually improved decision-making
Trade-offs between interpretability and performance

I shared an example:

After interpreting SHAP results from a credit model, we discovered that “years of employment” had an excessively high weight, misaligned with risk logic. Collaborating with the business team, we refined feature selection — improving both robustness and fairness.

Preparation Advice

If you’re targeting XAI-related roles, focus on these areas:

Core techniques: SHAP, LIME, Permutation, PDP, ICE, Integrated Gradients
Metrics: Fidelity, stability, and human interpretability
Project storytelling: Emphasize how your explanations aided business decisions
System design thinking: How to integrate interpretability into ML pipelines
Ethics & fairness: Handling sensitive attributes and bias mitigation

How Programhelp Supports XAI Interviews

A common issue for candidates in XAI interviews isn’t a lack of knowledge — it’s the inability to explain logic clearly under pressure.

Programhelp’s real-time voice coaching service assists during interviews by discreetly reminding you of key points — for instance, when the interviewer asks about SHAP, PDP, or fairness trade-offs.

This subtle voice guidance helps you stay calm, structured, and articulate — presenting yourself as a confident, research-minded candidate.

We’ve already helped many candidates secure offers from Meta, Capital One, Roche, Visa, and other companies with XAI-heavy roles.

If you’re preparing for similar interviews, you can explore our customized voice-assist service to boost your performance.