Causality-Inspired Taxonomy for Explainable Artificial Intelligence
CoRR(2022)
摘要
As two sides of the same coin, causality and explainable artificial
intelligence (xAI) were initially proposed and developed with different goals.
However, the latter can only be complete when seen through the lens of the
causality framework. As such, we propose a novel causality-inspired framework
for xAI that creates an environment for the development of xAI approaches. To
show its applicability, biometrics was used as case study. For this, we have
analysed 81 research papers on a myriad of biometric modalities and different
tasks. We have categorised each of these methods according to our novel xAI
Ladder and discussed the future directions of the field.
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关键词
explainable biometrics,learning,deep
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