Causality-Inspired Taxonomy for Explainable Artificial Intelligence

• URL: http://arxiv.org/abs/2208.09500v2
• Date: Mon, 4 Mar 2024 16:39:15 GMT
• Title: Causality-Inspired Taxonomy for Explainable Artificial Intelligence
• Authors: Pedro C. Neto, Tiago Gon\c{c}alves, Jo\~ao Ribeiro Pinto, Wilson Silva, Ana F. Sequeira, Arun Ross, Jaime S. Cardoso
• Abstract summary: We propose a novel causality-inspired framework for xAI that creates an environment for the development of xAI approaches. We have analysed 81 research papers on a myriad of biometric modalities and different tasks.
• Score: 10.241230325171143
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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