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- Summary and future considerations for AI fairness
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Classroom Contents
AI Bias and Fairness
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- 1 - Introduction and motivation
- 2 - What does "bias" mean?
- 3 - Bias in machine learning
- 4 - Bias at all stages in the AI life cycle
- 5 - Outline of the lecture
- 6 - Taxonomy types of common biases
- 7 - Interpretation driven biases
- 8 - Data driven biases - class imbalance
- 9 - Bias within the features
- 10 - Mitigate biases in the model/dataset
- 11 - Automated debiasing from learned latent structure
- 12 - Adaptive latent space debiasing
- 13 - Evaluation towards decreased racial and gender bias
- 14 - Summary and future considerations for AI fairness
