Overview
AI systems inherit the biases of their training data, raise hard questions about privacy and copyright, and carry real environmental costs. This session examines those issues honestly — then runs live bias experiments and works through ethical case studies so you can form and defend your own positions.
Learning objectives
- Explain where bias in AI systems comes from and demonstrate it with prompts.
- Describe the privacy, copyright, and environmental issues generative AI raises.
- Analyze an ethical case study and defend a position on it.
- Apply a personal checklist for responsible AI use in school and work.
Session agenda
- 6:00 – 6:10Warm-up
Most convincing wrong answers from homework.
- 6:10 – 7:15Lecture: Bias, privacy, copyright, environment
How training data encodes bias; what happens to the data you paste in; the copyright fights; energy and water costs of AI.
- 7:15 – 7:25Break
- 7:25 – 9:00Lab: Bias experiments & case studies
Run structured bias experiments with prompts and compare results across the class; small-group discussion of ethical case studies with positions reported out.
- 9:00 – 9:10Wrap-up
Case-study verdicts; preview automation.
Materials
- Bias experiment worksheet and case-study packet (provided in class).
- Any AI assistant account from previous sessions.
Homework
Write a one-page discussion of one ethical case study from class: the issue, the stakeholders, your position, and what evidence would change your mind. This is a graded writing assignment.
Detailed slides, lab worksheets, and demos for this session are in progress and will be posted here before class.