Edge cases form a very long tail of situations that must be dealt with by AI models. Traditional accuracy metrics do not predict how a model will behave when deployed in the real-world.
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Accuracy metrics fail to predict performance on long-tail real-world edge cases, both naturally occurring and adversarial. Robustness and security metrics are required to complete the Trustworthy AI picture.
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