Machine Learning Interview Questions – Part 5

Welcome to Part 5 of our Machine Learning Interview Questions Series. In this edition, we explore Responsible AI, including questions around fairness, data privacy, compliance, and model governance. These are must-know topics for data scientists and ML engineers working in finance, healthcare, edtech, or any domain where machine learning impacts human lives.

As AI adoption grows, so do the expectations for ethical, explainable, and legally compliant models. These questions help you prepare for interviews where you’re expected to not only build accurate models—but also responsible ones.

41. What is Responsible AI?

Responsible AI refers to the practice of designing, developing, and deploying AI systems in a way that is ethical, transparent, and accountable.

Key principles:

Fairness: Avoid discrimination
Transparency: Make models explainable
Accountability: Assign responsibility for decisions
Privacy: Protect user data
Robustness: Ensure safety and reliability

Organizations like Google, Microsoft, and IBM have published their Responsible AI frameworks to guide ML practitioners.

42. What is model fairness in machine learning?

Fairness in ML ensures that a model’s predictions are not biased against individuals or groups based on sensitive attributes like gender, race, or age.

Common Fairness Metrics:

Demographic parity
Equal opportunity
Equalized odds
Disparate impact

Bias can originate from data, labeling, features, or model behavior, so fairness must be addressed at multiple levels.

43. How do you detect and mitigate bias in ML models?

Bias Detection Techniques:

Compare model metrics across groups (e.g., accuracy for males vs females)
Use visualization tools (Fairlearn, Aequitas)
Feature importance analysis to identify indirect proxies

Mitigation Strategies:

Pre-processing: Balance datasets or reweigh samples
In-processing: Use fairness-constrained algorithms
Post-processing: Adjust predictions to reduce disparity

Tools like IBM AI Fairness 360 and Fairlearn help automate bias detection and mitigation.

44. What is model explainability and why is it important?

Model explainability refers to understanding why a model made a certain prediction.

Why it matters:

Builds trust with stakeholders
Helps with debugging and compliance
Required for regulated industries

Explainability Techniques:

SHAP (Shapley Additive Explanations)
LIME (Local Interpretable Model-agnostic Explanations)
Partial Dependence Plots
Counterfactual explanations

Interpretable models (e.g., decision trees, logistic regression) are often preferred in high-stakes environments.

45. What are data privacy concerns in ML?

ML models often rely on personal or sensitive data. Common privacy concerns include:

Unintended memorization of user data
Data leakage during feature engineering
Inference attacks where attackers extract training data from models

Mitigation Strategies:

Anonymization and pseudonymization
Differential Privacy
Federated Learning
Data minimization

Compliance with laws like GDPR and HIPAA is critical when working with sensitive information.

46. What is Differential Privacy?

Differential privacy is a formal privacy guarantee that ensures individual data points do not significantly influence the output of an algorithm.

Adds mathematical noise to query results
Makes it difficult to infer whether a specific record was included
Used by Apple, Google, and the US Census Bureau

It balances utility and privacy, especially in analytics and federated learning systems.

47. What is Federated Learning?

Federated Learning is a decentralized ML training technique where the model is trained locally on edge devices, and only model updates are shared with the central server.

Benefits:

Enhances data privacy
Reduces data transfer costs
Useful for on-device personalization (e.g., Gboard, Siri)

It’s often combined with secure aggregation and differential privacy for end-to-end secure learning.

48. What is model governance?

Model governance refers to the processes and policies for managing ML models across their lifecycle.

Includes:

Model approval workflows
Version tracking
Audit logs
Access controls
Compliance validation

Governance tools are especially important in finance, insurance, and healthcare where models must meet regulatory standards (e.g., SR 11-7, EU AI Act).

49. What are the key ML compliance standards and regulations?

Here are some industry regulations you should know:

GDPR (EU): Data protection and privacy, including Right to Explanation
HIPAA (US): Healthcare data security and privacy
CCPA (California): Consumer data protection
EU AI Act (Upcoming): Risk-tiered regulation of AI systems
SR 11-7 (US): Model risk management for banking

Compliance means models must be auditable, interpretable, and documented.

50. What documentation is essential for ML models in regulated environments?

Required documentation often includes:

Model cards: Summarize intended use, performance, and fairness
Datasheets for datasets: Describe how and why the dataset was created
Experiment tracking logs: Model versions, hyperparameters, metrics
Audit trails: Who trained/deployed the model, when, and why

Frameworks like Google’s Model Cards and Data Nutrition Labels help ensure transparency and reproducibility.

Conclusion

In Part 5 of our Machine Learning Interview Questions Series, we’ve shifted the focus from accuracy and performance to ethics, fairness, compliance, and accountability. These topics are no longer optional—they’re essential for building trustworthy, human-centered AI systems.

Understanding these concepts prepares you not just for technical interviews but also for leadership roles where decision-making around AI adoption, governance, and risk mitigation are key.

Stay tuned for Part 6 where we’ll dive into open-source ML tooling, model registries, experiment tracking, and best practices for ML collaboration in teams.

Resources

Ethics of artificial intelligence