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Explainability is not a technology issue — it is a human issue.
Therefore, it is incumbent on humans to be able to explain and understand how AI models come to the inferences that they do, said Madhu Narasimhan, EVP and head of innovation, strategy, digital and innovation at Wells Fargo.
“That’s a key part of why explainable AI becomes so important,” she emphasized to the audience during a fireside chat at today’s VentureBeat Transform 2023 event.
Narasimhan explained to the crowd and moderator Jana Eggers, cofounder and CEO of synaptic intelligence platform Nara Logics, that Wells Fargo did a “tremendous amount” of post hoc testing on its Fargo virtual assistant to understand why the model was interpreting language the way that it was.
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In building out models, the company concurrently builds out explainability and has an independent group of data scientists who separately validate them.
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