Definition
The model keeps calling small dogs cats. How do we fix it?
A model improves by learning from its mistakes: people find where it goes wrong, work out why, and give it more or better examples, often of exactly the cases it gets wrong. Then they train and test it again.
In simple words
If you keep confusing two words, your teacher shows you more examples of both until you can tell them apart. In the activity, the model learned only from big dogs, so it calls every small animal a cat. Add a few small dogs to its examples, test again, and watch the accuracy rise.
Why it matters
Every real AI system is improved this way. Teams collect the cases a model gets wrong and add better examples. This is also how bias is reduced: when a model fails for one group of people, examples from that group are added.
How it works
- Test the model and list its mistakes.
- Look for a pattern in the mistakes: are they all small dogs?
- Work out why: no small dogs in the training examples.
- Add labelled examples of the missing cases.
- Train and test again, and compare the scores.
Try it yourself
Fix a model that confuses two classes
Test the model, add the small dogs to its examples, then test again. Compare the two scores.
Common mistakes
- Adding random examples instead of the cases the model gets wrong.
- Fixing one mistake and never testing again. A change can break something else.
- Blaming the model. Usually the examples are the problem.
Check yourself
Summary
- Find the model's mistakes and look for a pattern in them.
- Add examples of the cases it gets wrong, then train and test again.
- This is also how unfair gaps in AI are fixed.
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