27 citations · 49 across the 9 of their papers we have counts for
12 papers
MetaExplainer: A Framework to Generate Multi-Type User-Centered Explanations for AI Systems
Shruthi Chari, Oshani Seneviratne, Prithwish Chakraborty +2
Explanations are crucial for building trustworthy AI systems, but a gap often exists between the explanations provided by models and those needed by users. To address this gap, we…
An Ontology-Enabled Approach For User-Centered and Knowledge-Enabled Explanations of AI Systems
Shruthi Chari
Explainable Artificial Intelligence (AI) focuses on helping humans understand the working of AI systems or their decisions and has been a cornerstone of AI for decades. Recent rese…
Informing clinical assessment by contextualizing post-hoc explanations of risk prediction models in type-2 diabetes
Shruthi Chari, Prasant Acharya, Daniel M. Gruen +8
Medical experts may use Artificial Intelligence (AI) systems with greater trust if these are supported by contextual explanations that let the practitioner connect system inference…
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…