Understand the BasicsMistakes, bias and uncertainty
Why AI can be confidently wrong, repeat unfair patterns or give a different answer the next time.
Plausible does not mean correct
Explain hallucinations and why confidence of tone is not evidence.
Patterns can carry bias
Show how data and design choices can reproduce unfair patterns.
Why answers can change
Explain variation in prompts and generated responses in plain language.
When and how to check
Give a simple verification approach and identify high-stakes situations.