Explainable AI (XAI)

Summary

Techniques (SHAP, LIME, attention visualization) for understanding why a model made a specific prediction — important for trust, debugging and regulatory requirements in sensitive domains.

How to Learn This

1Use SHAP or LIME to explain individual predictions from a classical ML model.
2Visualize attention weights to interpret a transformer model's focus on an example.
3Learn which domains (healthcare, finance) make explainability a hard requirement, not a nice-to-have.

Hallucination Detection & Mitigation

Data Privacy in AI Systems

More topics in Evaluation, Safety & Responsible AI

LLM Evaluation Metrics (BLEU, ROUGE, Perplexity)Human Evaluation & LLM-as-Judge Bias & Fairness in AI AI Safety Principles Guardrails & Content Moderation Hallucination Detection & Mitigation Data Privacy in AI Systems Responsible AI Governance

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