Teach the Cast
Some of AIForge’s cast are still learning. When you teach them, YOU learn it best — catch what they get wrong, then quiz them.
Teach Skew — the AI that got it wrong
You be the teacher — Skew is still learning. Helping Skew figure it out is the practice. (Only for you; nothing is saved.)
Teach the connections
Show how the AI-literacy ideas connect
You've helped them one at a time. The real understanding is how the ideas CONNECT — teach that next.
Feed (training data — the examples a model learns from; garbage in, garbage out) × Skew (bias — where an AI system goes wrong when its examples lean)
A hiring model keeps favoring one group. Why do Feed and Skew mean the fix starts with the data, not the output?
Feed then says: “A model's bias comes from nowhere in particular, so there is no point looking at its training data.” — is that right?
The link: bias is trained-in — the lean lives in the data first.
Skewed examples in, skewed decisions out.
Split (the train/test split — keep some examples hidden to tell learning from memorizing) × Sure (confidence — a model reports how sure it is)
A model is 99 percent confident but only on questions it has seen before. Why do Split and Sure work together?
Split then says: “If a model reports high confidence, you can trust it without testing on new data.” — is that right?
The link: hide some examples to check whether the confidence is learning or memorizing.
Test on the unseen before you trust the sure.
Capstone — audit one AI system together
Three checks on a model. Bring in the cast member for each.
Where the bias can sneak in. Who?
Who do you bring in?
How you tell real learning from memorizing. Who?
Who do you bring in?
What is at stake when it is deployed on real people. Who?
Who do you bring in?
The synthesis: check where bias enters, prove it really learned, and weigh what is at stake — how you audit an AI before trusting it.
Data, testing, and stakes — audit before you trust.
Try it in the AI tools →
