Measuring Impact and Advanced Readings

This unit integrates experimental methods for measuring return on investment (ROI) with synthesis of prior chapter notes and curated advanced readings on classical ML, deep learning, generative AI, and biologically inspired models. Students consolidate knowledge from the pathway, practice applying evaluation techniques, and explore real-world applications and implications for business and ethics.

Learning Objectives

• Analyze experimental results from A/B and controlled trials to calculate ROI and identify statistically significant business impacts
• Apply experimental design principles to plan and document an experiment that tests a behavioral AI intervention for a marketing or finance use case
• Demonstrate understanding of the strengths, limitations, and typical applications of classical machine learning, deep learning, generative AI, and biologically inspired models by summarizing use cases from the assigned readings
• Evaluate ethical, operational, and financial implications of deploying behavioral AI solutions, including risk, bias, and maintenance considerations, and recommend mitigation strategies