The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective. (arXiv:2202.01602v1 [cs.LG])

As various post hoc explanation methods are increasingly being leveraged to
explain complex models in high-stakes settings, it becomes critical to develop
a deeper understanding of if and when the explanations output by these methods
disagree with each other, and how such disagreements are resolved in practice.
However, there is little to no research that provides answers to these critical
questions. In this work, we introduce and study the disagreement problem in
explainable machine learning. More specifically, we formalize the …

arxiv explainable machine learning learning machine machine learning perspective