We addressed the problem of a lack of semantic representation for user-centric explanations and different explanation types in our Explanation Ontology (https://purl.org/heals/eo). Such a representation is increasingly necessary as explainability has become an important problem in Artificial Intelligence with the emergence of complex methods and an uptake in high-precision and user-facing settings. In this submission, we provide step-by-step guidance for system designers to utilize our ontology, introduced in our resource track paper, to plan and model for explanations during the design of their Artificial Intelligence systems. We also provide a detailed example with our utilization of this guidance in a clinical setting.
@article{arxiv.2010.01478,
title = {Explanation Ontology in Action: A Clinical Use-Case},
author = {Shruthi Chari and Oshani Seneviratne and Daniel M. Gruen and Morgan A. Foreman and Amar K. Das and Deborah L. McGuinness},
journal= {arXiv preprint arXiv:2010.01478},
year = {2020}
}