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Explainable AI for Natural Language Processing
Black box techniques, such as deep learning models and the use of language embeddings as features can substantially advance model quality, while their obfuscation of the process by which a model arrives at its results can be problematic. Therefore, In the broader AI community, this growing understanding of the importance of explainability has created an emerging field called Explainable AI (XAI).
Explanations can be categorized along two main aspects:
- The first distinguishes whether the explanation is for an individual prediction (local) or the model’s prediction process as a whole (global).
- The second differentiates between the explanation emerging directly from the prediction process (selfexplaining) versus requiring post-processing (posthoc).
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