most citedLeveraging Clinical Context for User-Centered Explainability: A Diabetes Use Case

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cs.AI2021

Semantic Modeling for Food Recommendation Explanations

Ishita Padhiar, Oshani Seneviratne, Shruthi Chari +2

With the increased use of AI methods to provide recommendations in the health, specifically in the food dietary recommendation space, there is also an increased need for explainabi…

cs.AI2020

Explanation Ontology: A Model of Explanations for User-Centered AI

Shruthi Chari, Oshani Seneviratne, Daniel M. Gruen +3

Explainability has been a goal for Artificial Intelligence (AI) systems since their conception, with the need for explainability growing as more complex AI models are increasingly…

cs.AI2020

Explanation Ontology in Action: A Clinical Use-Case

Shruthi Chari, Oshani Seneviratne, Daniel M. Gruen +3

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).…

cs.AI2020

Directions for Explainable Knowledge-Enabled Systems

Shruthi Chari, Daniel M. Gruen, Oshani Seneviratne +1

Interest in the field of Explainable Artificial Intelligence has been growing for decades and has accelerated recently. As Artificial Intelligence models have become more complex,…

cs.AI2020

Foundations of Explainable Knowledge-Enabled Systems

Shruthi Chari, Daniel M. Gruen, Oshani Seneviratne +1

Explainability has been an important goal since the early days of Artificial Intelligence. Several approaches for producing explanations have been developed. However, many of these…