Broad intuition · Study map 32 of 85
Reliability, fairness, and interpretability
Suggested preparation: Agents: models inside a control loop
Picture it
A model can sound certain outside its experience. It can also reproduce uneven patterns in its data. Interpretability tools provide clues about behavior; rigorous tests and interventions are needed to establish what those clues mean.
What to understand
- Hallucination, calibration, distribution shift, and subgroup evaluation
- Feature attribution, mechanistic interpretability, privacy, and adversarial inputs
Follow the references
1 · Course
Google · Machine Learning Crash Course ↗Read fairness and production ML systems; choose realistic evaluation slices.
You understand it when…
What evidence would make you trust a model improvement for a small but important group of users?
This is a study map. Follow the reference for the full explanation; use the question to test your mental model. Browse the reference shelf.