Data Privacy and AI Infrastructure

Recommended for: Technical courses, information literacy modules, or data ethics courses

Procedure

1. Assign students to investigate: Where are the physical servers located for AI systems they commonly use?
2. Have students map complete data flows: user interface → data transmission → processing location → storage → potential third-party access
3. Direct students to research who has access to their data, how it is used, and under what conditions it may be shared
4. Require students to review privacy policies and terms of service for their most frequently used AI systems
5. Task students with identifying what data is retained, retention duration, purposes for which data is used, and whether opt-out mechanisms exist
6. Facilitate class discussion synthesizing findings and analyzing implications

Discussion questions

• What happens to the prompts and content you submit to various AI systems? What happens if you run an LLM locally?
• How is user-generated data utilized to train, improve, or fine-tune AI models?
• Who owns the intellectual property rights to content generated through AI assistance?
• What are the specific implications for sensitive, confidential, or proprietary information?
• How do data privacy practices vary across different AI providers and jurisdictions?

Discussion questions

• What types of information should students and researchers avoid sharing with commercial AI systems?
• What practical strategies can protect privacy while still benefiting from AI tools?
• What are the broader implications of AI systems being trained on user data without explicit, informed consent?
• How do data privacy concerns and regulations differ across national and regional jurisdictions?

Assessment opportunity

Students produce a data privacy assessment comparing three AI systems they use, evaluating their data practices against established privacy frameworks.