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Found 8 papers, 4 papers with code
The XAI Alignment Problem: Rethinking How Should We Evaluate Human-Centered AI Explainability Techniques
Optimizing XAI for plausibility regardless of the model decision correctness also jeopardizes model trustworthiness, because doing so breaks an important assumption in human-human explanation that plausible explanations typically imply correct decisions, and vice versa; and violating this assumption eventually leads to either undertrust or overtrust of AI models.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
+1
Invisible Users: Uncovering End-Users' Requirements for Explainable AI via Explanation Forms and Goals
The EUCA study findings, the identified explanation forms and goals for technical specification, and the EUCA study dataset support the design and evaluation of end-user-centered XAI techniques for accessible, safe, and accountable AI.
Transcending XAI Algorithm Boundaries through End-User-Inspired Design
The boundaries of existing explainable artificial intelligence (XAI) algorithms are confined to problems grounded in technical users' demand for explainability.
Evaluating Explainable AI on a Multi-Modal Medical Imaging Task: Can Existing Algorithms Fulfill Clinical Requirements?
The evaluation and MSFI metric can guide the design and selection of XAI algorithms to meet clinical requirements on multi-modal explanation.
Explainable Artificial Intelligence (XAI)
Feature Importance
Guidelines and Evaluation of Clinical Explainable AI in Medical Image Analysis
Following the guidelines, we conducted a systematic evaluation on a novel problem of multi-modal medical image explanation with two clinical tasks, and proposed new evaluation metrics accordingly.
Computational Efficiency
Explainable artificial intelligence
+1
One Map Does Not Fit All: Evaluating Saliency Map Explanation on Multi-Modal Medical Images
The maps highlight important features for AI model's prediction.
EUCA: the End-User-Centered Explainable AI Framework
The ability to explain decisions to end-users is a necessity to deploy AI as critical decision support.
Decision Making
Explainable artificial intelligence
Human-Computer Interaction
Artificial Intelligence in Glioma Imaging: Challenges and Advances
We believe that these technical approaches will facilitate the development of a fully-functional AI tool in the clinical care of patients with gliomas.
