Human Factors in Artificial Intelligence

4 units · Graduate courses (200-299)

Lecture, four hours; outside study, eight hours. Preparation: familiarity with algorithms such as linear regression, decision trees, clustering, and neural networks; competence in probability distributions, statistical inference, and hypothesis testing; foundational knowledge of modern artificial intelligence/machine learning (AI/ML) comparable to an introductory undergraduate course; and proficiency in Python, including libraries such as NumPy, pandas, and scikit-learn. Examination of how humans influence and are influenced by AI across its lifecycle—from data collection and annotation to model development and deployment. Topics include fairness and bias, trust and reliance, interpretability, and interactivity. Emphasis on human-centered design, ethics, and societal impact. Through lectures and projects, including creative formats such as debates on human–AI interaction, students develop the skills to design and evaluate AI systems that are both technically sound and user-centered. Letter grading.