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dc.contributor.authorChari, Shruthi
dc.contributor.authorSeneviratne, Oshani
dc.contributor.authorGruen, Daniel
dc.contributor.authorForeman, Morgan
dc.contributor.authorDas, Amar
dc.contributor.authorMcGuinness, Deborah
dc.date.accessioned2022-02-15T17:29:40Z
dc.date.available2022-02-15T17:29:40Z
dc.date.issued2020-11-01
dc.identifier.other12
dc.identifier.urihttps://www.researchgate.net/publication/344485948_Explanation_Ontology_in_Action_A_Clinical_Use-Case
dc.description.abstractWe 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.
dc.relation.urihttps://tw.rpi.edu/project/HEALS
dc.subjectHealth Empowerment by Analytics, Learning, and Semantics (HEALS)
dc.titleExplanation Ontology in Action: A Clinical Use-Case


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