Responsible Generative AI Practices

This unit examines the ethical, legal, societal, and economic implications of generative AI while teaching practical methods for producing trustworthy AI outputs. Students learn advanced prompt engineering, model tailoring, quality control, and governance strategies to responsibly deploy AI in business contexts.

Learning Objectives

• Analyze ethical and legal issues associated with generative AI (including intellectual property, bias, misinformation, privacy, safety, and environmental impacts) and propose concrete mitigation strategies for business applications
• Evaluate generative AI’s effects on jobs, industry structure, and organizational dependency, and recommend oversight, self-regulation, or policy approaches that balance innovation and risk
• Apply advanced prompt engineering, model specialization, and quality-control techniques to create accurate, transparent, and auditable AI-generated artifacts (e.g., white papers, reports, blogs, and visual assists)
• Demonstrate implementation of human-in-the-loop oversight and accountability practices—such as bias testing, attribution, IP compliance, and journalism ethics—by producing a project-ready checklist or governance draft