Fairness, Bias Mitigation, and Responsible AI in Production Questions
Understand bias sources in ML systems and fairness metrics (demographic parity, equalized odds, calibration across groups). Design bias testing and monitoring. Discuss mitigation strategies: diverse data, algorithmic debiasing, and post-processing. For Staff-level, embed responsible AI practices into organizational processes.
EasyTechnical
52 practiced
You are interviewing for an AI Engineer role working on a binary loan-approval model. Define and compare the following fairness metrics: demographic parity (statistical parity), equalized odds, and calibration across groups. For each metric explain (a) the formal definition, (b) an example scenario where it is appropriate, and (c) a key limitation or failure mode when applied in production.
HardTechnical
56 practiced
Formulate demographic parity as a constrained optimization for logistic regression. Derive the Lagrangian combining the logistic loss and a constraint that forces parity between two groups on average prediction probability. Describe an algorithm to solve the primal-dual problem and practical considerations for convergence and hyperparameter tuning.
MediumTechnical
64 practiced
Multi-class classification: propose practical definitions and evaluation procedures for fairness when the model predicts one of K classes. Describe (a) one-vs-rest parity checks, (b) per-class equalized odds extension, and (c) how to present multi-class fairness results in a concise dashboard.
MediumTechnical
59 practiced
Propose a practical checklist and automation approach to integrate fairness checks into an ML CI/CD pipeline. Include unit tests for metrics, integration tests with production-like data, gating criteria, artifact generation (model card), and how to make tests deterministic despite data variance.
HardSystem Design
67 practiced
Design a scalable continuous fairness monitoring pipeline for a model serving 100 million users with 10,000 requests per second. Describe streaming vs batch components, aggregation windows, storage for raw logs and aggregates, exact vs approximate counters, privacy and access controls, alerting thresholds, and cost-control design choices.
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