Explainable AI: Language Models

#artificialintelligence 

Just like a coin, explainability in AI has two faces -- one it shows to the developers (who actually build the models) and the other to the users (the end customers). The former face (IE i.e. intrinsic explainability) is a technical indicator to the builder that explains the working of the model. Whereas the latter (EE i.e. extrinsic explainability) is proof to the customers about the model's predictions. While IE is required for any reasonable model improvement, we need EE for factual confirmation. A simple layman who ends up using the model's prediction needs to know why is the model suggesting something.

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