Big ideas in machine learning, simply explained
The rapidly increasing usage of machine learning raises complicated questions: How can we tell if models are fair? Why do models make the predictions that they do? What are the privacy implications of feeding enormous amounts of data into models?
This ongoing series of interactive, formula-free essays will walk you through these important concepts.
Models trained on real-world data can encode real-world bias. Hiding information about protected classes doesn’t always fix things—sometimes it can even hurt.