AIForge ✕
◆ AIForge Learn 🎓 Teach 🗺️ Adventure 🤝 Together 🔁 Review ⋯ More 🧭 My Journey 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. (Skew's progress stays on this device; nothing is sent anywhere.)
🎓 In a minute you'll be the teacher. Get ready to teach Skew: bias from skewed training data.
A model learns only from the examples it's given. If the examples are lopsided, the model is too — more of the same won't fix it.
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?
Skew's bias is the lean in Feed's training data made mechanical — the model learned the imbalance in its examples, so you fix the data at the source Bias appears randomly and has nothing to do with the training examples Training data and bias are unrelated parts of an AI system
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?
No — Skew's bias traces straight back to the lean in Feed's training data; that is where the fix begins Right, the data is irrelevant to bias
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's hidden test data reveals whether Sure's confidence is real learning or just memorization — you validate the confidence on unseen examples A high confidence number always means the model truly learned, so testing is unnecessary Confidence and the train/test split have nothing to do with each other
Split then says: “If a model reports high confidence, you can trust it without testing on new data.” — is that right?
No — Split's held-out test is exactly what tells you whether Sure's confidence is trustworthy Right, high confidence needs no testing
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?
Feed — the training data Sort — the classifier Sure — confidence
How you tell real learning from memorizing. Who?
Who do you bring in?
Split — the train/test split Cue — the features Edge — model limits
What is at stake when it is deployed on real people. Who?
Who do you bring in?
Stake — the ethics Feed — the training data Split — the train/test split
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 →
Reading Access Make reading comfier. Saved on this device only — nothing leaves your device.
Line spacing Normal Roomy Roomiest
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Font Default OpenDyslexic Book
Reading tools Reading ruler Focus mode Read aloud Bigger tiles
Motion & sound No motion Sound off
Game look Usual Bigger and calmer
Same ideas with a younger band's look: bigger type and buttons, a rounder font, slower motion. For any child who wants it.
Heavy features Auto Full Lighter
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