ProtoShotXAI: Using Prototypical Few-Shot Architecture for Explainable AI

Unexplainable black-box models create scenarios where anomalies cause deleterious responses, thus creating unacceptable risks. These risks have motivated the field of eXplainable Artificial Intelligence (XAI) which improves trust by evaluating local interpretability in black-box neural networks. Unfortunately, the ground truth is unavailable for the model's decision, so evaluation is limited to qualitative assessment. Further, interpretability may lead to inaccurate conclusions about the model or a false sense of trust. We propose to improve XAI from the vantage point of the …

architecture artificial artificial intelligence box decision evaluation explainable ai explainable artificial intelligence few-shot intelligence interpretability networks neural networks responses risks trust xai