Start by clarifying the problem scope and success metrics, then walk through the ML lifecycle: data collection, feature engineering, model selection, evaluation, and monitoring. Emphasize trade-offs between precision and recall, and how you'd handle concept drift and adversarial behavior. Conclude with a deployment and feedback loop strategy.
Pro tip: Frame the problem as a ranking and triage system rather than a binary classifier, and discuss how you'd incorporate human-in-the-loop review to balance automation with accuracy. Highlight the importance of explainability for trust and regulatory compliance.
Define what 'fake news' means, the scale of the problem, and the key metrics (e.g., precision, recall, F1, AUC, or business metrics like user reports). Consider the cost of false positives vs. false negatives.
Identify data sources (post content, user engagement, source credibility, propagation patterns). Engineer features from text (TF-IDF, embeddings), metadata, and graph-based features (sharing network).
Choose models (e.g., gradient boosted trees, deep learning for text, graph neural networks). Address class imbalance, use ensemble methods, and consider multi-modal approaches. Train with cross-validation and handle adversarial examples.
Use offline metrics (precision-recall curves, AUC) and online A/B testing. Incorporate human evaluation and measure real-world impact (e.g., reduction in spread). Consider fairness and bias across demographics.
Deploy with a feedback loop: flag content for review, collect labels, and retrain periodically. Monitor for drift using statistical tests and performance metrics. Implement safeguards against adversarial attacks and model degradation.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.