AI Bias and Fairness

Explore the critical topic of AI bias and fairness in this 43-minute lecture from MIT's Introduction to Deep Learning course. Delve into the various types of biases in machine learning, including interpretation-driven and data-driven biases. Learn about bias at different stages of the AI lifecycle and discover strategies to mitigate these issues. Examine techniques such as automated debiasing from learned latent structure and adaptive latent space debiasing. Gain insights into evaluating AI systems for reduced racial and gender bias. Conclude with a summary of key points and future considerations for ensuring fairness in artificial intelligence.

​ - Introduction and motivation
- What does "bias" mean?
- Bias in machine learning
- Bias at all stages in the AI life cycle
- Outline of the lecture
- Taxonomy types of common biases
- Interpretation driven biases
- Data driven biases - class imbalance
- Bias within the features
- Mitigate biases in the model/dataset
- Automated debiasing from learned latent structure
- Adaptive latent space debiasing
- Evaluation towards decreased racial and gender bias
- Summary and future considerations for AI fairness