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Module 3 · Stewardship: make it durableWed, Oct 760 min

Judgment under uncertainty

Examine the moments where an AI system can create harm or confusion. Define the human judgment, evidence, and recourse each moment requires.

Faculty: Prof. Giovanni Parmigiani

Learning objectives

  • ▸Apply a bias assessment to an AI use case
  • ▸Define fairness and transparency for their context
  • ▸Design consent models

Key concepts

AI ethics frameworksBias assessmentFairness & transparencyConsent models

Memorize this

🧠 Ethics screen🧠 Bias assessment🧠 Consent models

Your study flow

  1. 1. Read

    Read the session guide above. Note the ideas that change how you see your organization's current AI question.

  2. 2. Test

    Take the module quiz in Learn & Memorize to check understanding.

  3. 3. Drill

    Run the spaced-repetition flashcards to lock in the terms from this module.

📝 Test this session

Question 1 of 3Judgment under uncertainty

A bias assessment checks:

Flashcards for this module

  • Risk register
    A living table of AI risks: category, likelihood × impact scoring, owner, and mitigation. The core of a governance framework that survives audit.
  • EU AI Act risk tiers
    Unacceptable (banned), High (strict obligations), Limited (transparency), Minimal. Classification depends on the use case and sector. Timeline enforced in phases.
  • NIST AI RMF
    U.S. AI Risk Management Framework: govern, map, measure, manage. The free anchor reference for responsible AI governance.
  • Bias assessment
    Systematically checking an AI system's training data, design, and outputs for unfair outcomes across groups: before and after deployment.
  • Consent model
    Defines how and when people agree to their data being used by AI: required for trust and, in regulated contexts, compliance.
  • Attribution problem
    Proving how much of an outcome is caused by AI vs other factors. The hardest part of AI ROI: requires a baseline and control/cohort thinking.
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