DEI, Well-being, and AI Ethics

This unit examines how data science principles inform the design, evaluation, and measurement of DEI and employee well-being programs, and explores the opportunities and risks that AI introduces in these contexts. Building on prior foundational analytics skills, students will develop program evaluation plans, craft metrics and data-collection strategies, analyze case studies, and assess AI-related ethical and technical risks in preparation for implementation-focused work in Unit 4.

Learning Objectives

• Analyze distinctions between correlation and causation in DEI and well-being data and identify common confounders and limitations that affect interpretation
• Apply program evaluation fundamentals to design a measurable DEI or well-being initiative, including clear business questions, proactive data collection strategies, and key performance metrics
• Evaluate real-world case studies of data-informed DEI and well-being programs to derive evidence-based recommendations and create an implementation-ready measurement plan
• Demonstrate the ability to identify AI-related risks (including bias, model deployment harms, and deepfakes) and propose technical and policy mitigations to protect fairness, privacy, and employee well-being