Explanation Ontology in Action: A Clinical Use-Case
This addresses the problem of explainability in AI for system designers, but it is incremental as it builds on a previously introduced ontology.
The paper tackled the lack of semantic representation for user-centric explanations in AI by providing guidance for system designers to use their Explanation Ontology, demonstrated with a clinical use-case.
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.