Authors: Markus Iser
Abstract: Automated reasoning is a key technology in the young but rapidly growing
field of Explainable Artificial Intelligence (XAI). Explanability helps build
trust in artificial intelligence systems beyond their mere predictive accuracy
and robustness. In this paper, we propose a cycle of scientific discovery that
combines machine learning with automated reasoning for the generation and the
selection of explanations. We present a taxonomy of explanation selection
problems that draws on insights from sociology and cognitive science. These
selection criteria subsume existing notions and extend them with new
properties.
Source: http://arxiv.org/abs/2407.17454v1
