15 citations · 20 across the 4 of their papers we have counts for
6 papers
Leveraging Clinical Context for User-Centered Explainability: A Diabetes Use Case
Shruthi Chari, Prithwish Chakraborty, Mohamed Ghalwash +7
Academic advances of AI models in high-precision domains, like healthcare, need to be made explainable in order to enhance real-world adoption. Our past studies and ongoing interac…
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…
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…
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).…
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,…
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…