Don’t be wrong because you might be fooled: Tips on how secure your ML model

1. Miss-labeled/Confusing Training Data

2. Is this a “cat” or a “dog”?

3. Is this a “bird” or an “airplane”?

👉 Good data means a good model: spend some time investigating your data and try to identify if there are any systematic errors in your training set.

👉 Use explanation methods as a debugger, in order to understand why your model model misses certain groups of instances more than others

👉 Adversarial attacks are a cost-effective way to check the adversarial robustness of your model.



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