Learning from the best: Rationalizing prediction by adversarial information calibration
Explaining the predictions of AI models is paramount in safety-critical applications, such as in legal or medical domains. One form of explanation for a prediction is an extractive rationale, i.e., a subset of features of an instance that lead the model to give its prediction on the instance. Previo...
| Main Authors: | , , |
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| Format: | Conference item |
| Language: | English |
| Published: |
AAAI Press
2021
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