Generative AI Ethics and Practice

This unit explores the socio-technical landscape of generative AI, covering exponential trends in computing and data, ethical and legal concerns (IP, bias, misinformation, privacy), and environmental and workforce impacts. Students will also develop practical skills in prompt engineering, model tailoring, multimodal outputs, human-in-the-loop quality control, and safe integration of AI agents and tools to prepare for advanced technical work.

Learning Objectives

• Analyze how exponential growth in computing, data, and R&D investment affects the scalability, resource requirements, and environmental impacts of generative AI systems.
• Evaluate ethical, legal, and societal implications of generative AI — including intellectual property, bias and fairness, misinformation, privacy, safety, and workforce displacement — and propose mitigation or governance strategies.
• Apply prompt engineering and model-customization techniques to produce, assess, and refine multimodal AI outputs (text, code, images) while documenting quality-control measures and human oversight decisions.
• Demonstrate methods for responsibly integrating generative AI with other technologies (autonomous agents, visual assistants, APIs), producing reproducible artifacts (reports, backtests), and implementing operational safeguards for security and compliance.