Machine Learning / Evidence and operational responsibility
Machine Learning β Evidence and operational responsibility
Uncertainty, calibration, fairness, explanations, deployment contracts, and drift.
What you will learn
- Uncertainty, calibration, fairness, explanations, deployment contracts, and drift.
Evaluation uncertainty and model comparisons
Report the uncertainty and sampling unit behind a score, and compare models through matched evidence rather than isolated winning numbers.
Start learningCalibration and decision costs
Assess probability meaning separately from ranking, and connect thresholds to declared costs and a representative population.
Start learningFairness, slices, and potential harms
Assess context-dependent harms and subgroup evidence without reducing fairness to one aggregate number or a proxy-removal rule.
Start learningExplanations and causal limits
Explain the behavior of a specific model under explicit assumptions without turning associations or perturbations into causal guarantees.
Start learningDeployment contracts and reproducibility
Package the complete learned procedure with a validated inference schema, provenance, and a reviewable rollback boundary.
Start learningMonitoring, drift, and retraining
Separate data and outcome signals, investigate changes under stable definitions, and govern retraining through a verifiable release process.
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