●Recent trends on XAI
●Method 1: LIME/SHAP
Example: Image classification
●Method 2: ABN for image classification
Generally speaking, AI is a blackbox.
We want AI to be explainable because…
1. Users should trust AI to actually use it (prediction itself, or model)
Ex: diagnosis/medical check, credit screening
G. Tolomei, et. al., arXiv:1706.06691
People want to know why they were rejected by AI screening, and what they should do in order to pass the screening.
2. It helps to choose a model from some candidates
Classifier of text to “Christianity” or “Atheism” (無神論)
Both model give correct classification, but it is apparent that model 1 is better than model 2.