Week 2 — Bias & Deepfakes
Learning objectives
- Explain why AI models inherit bias from their training data.
- Identify visual, audio, and contextual cues that suggest a deepfake.
- Understand that “looks real” is no longer a reliable test for “is real.”
Core concepts
Two related problems sit at the heart of this week. Bias is structural — an AI trained mostly on one kind of voice, face, or worldview will quietly amplify it. Deepfakes are deliberate — synthetic images, audio, and video designed to look authentic. Both rely on the same underlying tech, and both require the same defence: lateral verification rather than gut feeling.
Discussion prompts
- If an AI was trained mostly on text from one country, one language, or one political viewpoint, how might its answers be skewed? Whose voices might be missing?
- Have either of you seen something online recently you suspected was AI-generated? What tipped you off — or what failed to?
- Is there a difference between a deepfake made for satire, one made for a scam, and one made to harass someone? Should all three be treated the same way?
- If a deepfake of you (or a friend) appeared online tomorrow, what would you want to happen? Who would you tell first?
At-home activity: “Bias audit + deepfake hunt”
Part A — Bias audit (20 min). Ask one image generator (e.g. ChatGPT’s image tool, Gemini, or a free alternative) to produce images for ten neutral prompts: “a CEO,” “a nurse,” “a criminal,” “a scientist,” “a person cleaning,” “a wedding,” “a beautiful house,” “a homeless person,” “a teenager studying,” “a family at dinner.” Don’t add adjectives. Look at the results together: what patterns appear in gender, race, age, body type, setting, wealth? What’s missing?
Part B — Deepfake hunt (20 min). Visit MIT’s “Detect Fakes” media literacy site and work through their examples together. Then scroll your normal social feeds for 10 minutes specifically looking for AI-generated images. Compare notes on what gave them away.
Parent resource list
- MIT — Media Literacy in the Age of Deepfakes — interactive lessons and examples designed for classroom and home use.
- Parents Pass It On — How to Explain AI Bias to Teens in Simple Terms — concrete conversation scripts and comparison exercises.
- Common Sense Media — Deepfakes, Distrust and Disinformation — parent-facing primer on synthetic media.
- Algorithmic Justice League — Joy Buolamwini’s organisation; strong real-world examples of facial-recognition bias.
Reflection
Name one piece of media you’ve seen this week that you now want to re-examine.