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Informing clinical assessment by contextualizing post-hoc explanations of risk prediction models in type-2 diabetes
All of these steps were performed in engagement with medical experts, including a final evaluation of the dashboard results by an expert medical panel.
Leveraging Clinical Context for User-Centered Explainability: A Diabetes Use Case
To enable the adoption of the ever improving AI risk prediction models in practice, we have begun to explore techniques to contextualize such models along three dimensions of interest: the patients' clinical state, AI predictions about their risk of complications, and algorithmic explanations supporting the predictions.
Explanation Ontology: A Model of Explanations for User-Centered AI
With greater adoption of these systems and emphasis on user-centric explainability, there is a need for a structured representation that treats explainability as a primary consideration, mapping end user needs to specific explanation types and the system's AI capabilities.
Explanation Ontology in Action: 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).
Foundations of Explainable Knowledge-Enabled Systems
Additionally, borrowing from the strengths of past approaches and identifying gaps needed to make explanations user- and context-focused, we propose new definitions for explanations and explainable knowledge-enabled systems.
Directions for Explainable Knowledge-Enabled Systems
Interest in the field of Explainable Artificial Intelligence has been growing for decades and has accelerated recently.
