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Learn/Course 20
Production · weeks 24

Governance, fairness, privacy & security

A high-performing system creates subgroup harm and leaks sensitive information.

concept:fairnessconcept:privacyfailure:supply-chain
Opening production brief

A measurable problem, before a model.

Users

Operators, product owners, and people affected by the responsible ml decision.

Data

intended use, equalized odds, intersectional slices, privacy threat modeling with version, owner, event time, and availability contracts.

Constraints

A high-performing system creates subgroup harm and leaks sensitive information. Every solution must state latency, cost, capacity, and fallback limits.

Success

Offline evidence plus a deployable resolution: A launch review covering fairness, privacy, licensing, abuse, security, and response.

CHAPTER 1

Frame and baseline

Turn the production problem into explicit data, metric, and baseline contracts.

  1. 01

    Inventory, risk tiers, accountability, evidence

    Use inventory, risk tiers, accountability, evidence to move the responsible ml production brief toward a defensible release.

    65–90 min · checkpoint · deterministic lab
  2. 02

    Fairness definitions, slices, thresholds, tradeoffs

    Use fairness definitions, slices, thresholds, tradeoffs to move the responsible ml production brief toward a defensible release.

    65–90 min · checkpoint · deterministic lab
CHAPTER 2

Build and stress

Formalize the machinery, test counterexamples, and expose system limits.

  1. 03

    Privacy minimization, retention, federated learning, DP

    Use privacy minimization, retention, federated learning, dp to move the responsible ml production brief toward a defensible release.

    65–90 min · checkpoint · deterministic lab
  2. 04

    Poisoning, evasion, extraction, and LLM threats

    Use poisoning, evasion, extraction, and llm threats to move the responsible ml production brief toward a defensible release.

    65–90 min · checkpoint · deterministic lab
CHAPTER 3

Resolve and operate

Join model behavior to architecture, observability, rollout, and rollback.

  1. 05

    Red 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.

    65–90 min · checkpoint · deterministic lab
  2. HW

    Model assurance package

    Produce intended-use, fairness, privacy, security, human-review, monitored-rollout, and release evidence.

    Rubric · staged hints · reference resolution
Prerequisite guidance

2 concepts make this faster.

Prerequisites are guidance, never hard gates.

C18Serving systemsBatch and online paths with batching, autoscaling, load tests, canaries, and rollback.C19ReliabilityJoined data/model/service telemetry, drift policy, experiments, alerts, and runbooks.
Ready to work the problem?

Inventory, risk tiers, accountability, evidence

Start lesson 01