Lecture 20

Equality of opportunity in supervised learning

Matthew Youngbar

Lecture 21 (I)

Fairness through awareness

Farhad Mohsin

Lecture 21 (II)

Learning fair representations

Farhad Mohsin

Lecture 22 (I)

Counterfactual fairness

Rhea Banerjee

Lecture 22 (II)

Fairness constraints: Mechanisms for fair classification

Rhea Banerjee

Lecture 23

Transparency, explainability, trust, transparency, interpretability

Lecture 24 (I)

Towards a rigorous science of interpretable machine learning

Zirui Yan

Lecture 24 (II)

Algorithmic transparency via quantitative input influence: Theory and experiments with learning systems

Zirui Yan

Lecture 25 (I)

A unified approach to interpreting model predictions

Lucky Yerimah

Lecture 25 (II)

Why should I trust you? Explaining the predictions of any classifier

Lucky Yerimah

Lecture 26

Explaining explanations in AI

Andrew Nguyen