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Explainable artificial intelligence

XAI refers to methods and techniques in the application of artificial intelligence (AI) such that the results of the solution can be understood by humans. It contrasts with the concept of the "black box" in machine learning where even its designers cannot explain why an AI arrived at a specific decision. XAI may be an implementation of the social right to explanation. XAI is relevant even if there is no legal right or regulatory requirement—for example, XAI can improve the user experience of a product or service by helping end users trust that the AI is making good decisions. This way the aim of XAI is to explain what has been done, what is done right now, what will be done next and unveil the information the actions are based on. These characteristics make it possible (i) to confirm existing knowledge (ii) to challenge existing knowledge and (iii) to generate new assumptions.

Papers

Showing 251260 of 971 papers

TitleStatusHype
Automatic Diagnosis of Myocarditis Disease in Cardiac MRI Modality using Deep Transformers and Explainable Artificial Intelligence0
Disproving XAI Myths with Formal Methods -- Initial Results0
Distance-Restricted Explanations: Theoretical Underpinnings & Efficient Implementation0
Distributed Linguistic Representations in Decision Making: Taxonomy, Key Elements and Applications, and Challenges in Data Science and Explainable Artificial Intelligence0
Enhanced Prototypical Part Network (EPPNet) For Explainable Image Classification Via Prototypes0
ApproXAI: Energy-Efficient Hardware Acceleration of Explainable AI using Approximate Computing0
CAT: Concept-level backdoor ATtacks for Concept Bottleneck Models0
AcME-AD: Accelerated Model Explanations for Anomaly Detection0
Case-based Explainability for Random Forest: Prototypes, Critics, Counter-factuals and Semi-factuals0
Applying XAI based unsupervised knowledge discovering for Operation modes in a WWTP. A real case: AQUAVALL WWTP0
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