Ethical Machine Learning in Human Systems

3-0-9 units · Graduate

Focuses on the human-facing considerations in the pipeline of machine learning (ML) development in human-facing systems like healthcare, employment, and education. Topics covered include: algorithmic fairness, sources of model error/bias, model robustness, human/model evaluations, and policy. Discusses pertinent issues co-occurring in society; projects emphasize the technical and ethical challenges of using machine learning in human systems.