Welcome to our session on Algorithmic Fairness. This course provides a thorough understanding of bias and fairness in AI systems.
We’ll begin with real-world examples highlighting the consequences of discrimination in AI, and discuss the concept of protected features. Then, we’ll delve into the intricacies of defining and measuring unfairness in algorithms, comparing group vs individual, and causal vs observational fairness.
The course also explores the origins of bias, detailing how it can unintentionally seep into AI systems. We’ll look at data and model design as potential sources and discuss practical strategies to mitigate these biases.
Lastly, we’ll tie fairness to other crucial AI concepts and explore alternative views on fairness, moving beyond standard metrics. We aim to help you create AI systems that not only function efficiently but are also fair and ethical.
