Issues regarding explainable AI involve four components: users, laws & regulations, explanations and algorithms. Together these components provide a context in which explanation methods can be evaluated regarding their adequacy. The goal of this chapter is to bridge the gap between expert users and lay users. Different kinds of users are identified and their concerns revealed, relevant statements from the General Data Protection Regulation are analyzed in the context of Deep Neural Networks (DNNs), a taxonomy for the classification of existing explanation methods is introduced, and finally, the various classes of explanation methods are analyzed to verify if user concerns are justified. Overall, it is clear that (visual) explanations can be given about various aspects of the influence of the input on the output. However, it is noted that explanation methods or interfaces for lay users are missing and we speculate which criteria these methods / interfaces should satisfy. Finally it is noted that two important concerns are difficult to address with explanation methods: the concern about bias in datasets that leads to biased DNNs, as well as the suspicion about unfair outcomes.
@article{arxiv.1803.07517,
title = {Explanation Methods in Deep Learning: Users, Values, Concerns and Challenges},
author = {Gabrielle Ras and Marcel van Gerven and Pim Haselager},
journal= {arXiv preprint arXiv:1803.07517},
year = {2018}
}
Comments
14 pages, 1 figure, This article will appear as a chapter in Explainable and Interpretable Models in Computer Vision and Machine Learning Springer series on Challenges in Machine Learning