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AI FLUENCY — CHEAT SHEET
AI Ethics & Governance
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Bias, fairness metrics, EU AI Act, watermarking, and responsible deployment.
6 terms

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* Algorithmic Bias
  Systematic errors in a model's outputs that unfairly advantage or disadvantage particular groups, often reflecting biases in training data.
  e.g. A hiring model trained on historical data disadvantaging women applicants.

* Differential Privacy
  A mathematical guarantee that adding or removing any single record barely changes a model's output distribution, protecting individual privacy.
  e.g. Adding calibrated noise during training so no one person's data can be reverse-engineered.

* Disparate Impact
  A facially neutral policy or practice that disproportionately harms a protected group, even without intent to discriminate.
  e.g. A loan-scoring rule that unintentionally denies a protected group at a higher rate.

* EU AI Act
  The European Union's binding law that classifies AI systems by risk level and sets requirements scaled to that risk.
  e.g. Banning real-time biometric surveillance in public spaces for law enforcement, with narrow exceptions.

* Explainability (Interpretability)
  The ability to understand and communicate why a model produced a particular output or decision.
  e.g. Highlighting which words in a resume most influenced a hiring model's score.

* Fairness Metric
  A formal criterion, such as demographic parity or equalized odds, used to check whether a model treats different groups equitably.
  e.g. Checking that a loan model approves applicants from different groups at similar rates.

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