Evaluating AI for Societal Good

This unit examines real-world AI for Good initiatives, focusing on stakeholder collaboration, measurable outcomes, and the data and ethical challenges that arise in health and human services. Students will analyze case studies, apply common evaluation metrics, and design collaborative plans that mitigate risks while maximizing societal benefit.

Learning Objectives

• Analyze case studies from Microsoft’s AI for Good Lab to identify project goals, stakeholders, intended health or social impacts, and evidence of outcomes
• Evaluate ethical, legal, and practical challenges in deploying AI within healthcare and human services, including data privacy, bias, accountability, and lessons learned
• Apply commonly used quantitative and qualitative measures to sample datasets and AI interventions to assess effectiveness, equity, and societal benefit
• Demonstrate the ability to design a collaborative project plan that specifies roles, data needs, evaluation metrics, and risk mitigation strategies for an AI-for-good health informatics initiative