Fairness
Fairness in the context of production machine-learning systems.
Lessons
Formal explanations, examples, simulations, and checkpoints.
Inventory, risk tiers, accountability, evidence
Use inventory, risk tiers, accountability, evidence to move the responsible ml production brief toward a defensible release.
Governance, fairness, privacy & securityFairness definitions, slices, thresholds, tradeoffs
Use fairness definitions, slices, thresholds, tradeoffs to move the responsible ml production brief toward a defensible release.
Governance, fairness, privacy & securityPrivacy minimization, retention, federated learning, DP
Use privacy minimization, retention, federated learning, dp to move the responsible ml production brief toward a defensible release.
Governance, fairness, privacy & securityPoisoning, evasion, extraction, and LLM threats
Use poisoning, evasion, extraction, and llm threats to move the responsible ml production brief toward a defensible release.
Governance, fairness, privacy & securityRed teams, human oversight, audit, and response
Use red teams, human oversight, audit, and response to move the responsible ml production brief toward a defensible release.
Courses & assignments
Dependency-authoritative learning units.
Casebook
Reported facts and course reconstructions.
Auditing and retiring an image-cropping algorithm
Sometimes product redesign and user agency are stronger mitigations than model tuning.
LinkedIn · Fairness, privacy & securityA fairness toolkit for large-scale AI
Fairness infrastructure must be context-aware, governed, and integrated into decisions.
Stripe · Fairness, privacy & securityStripe Radar: learning fraud under adversarial pressure
Risk modeling connects delayed supervision, adversarial drift, calibrated decisions, and layered controls.