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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

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Showing 251–275 of 971 papers

TitleStatusHype
Automatic Diagnosis of Myocarditis Disease in Cardiac MRI Modality using Deep Transformers and Explainable Artificial Intelligence—0
Disproving XAI Myths with Formal Methods -- Initial Results—0
Distance-Restricted Explanations: Theoretical Underpinnings & Efficient Implementation—0
Distributed Linguistic Representations in Decision Making: Taxonomy, Key Elements and Applications, and Challenges in Data Science and Explainable Artificial Intelligence—0
Evaluation of Human-Understandability of Global Model Explanations using Decision Tree—0
ApproXAI: Energy-Efficient Hardware Acceleration of Explainable AI using Approximate Computing—0
CAT: Concept-level backdoor ATtacks for Concept Bottleneck Models—0
AcME-AD: Accelerated Model Explanations for Anomaly Detection—0
Case-based Explainability for Random Forest: Prototypes, Critics, Counter-factuals and Semi-factuals—0
Applying XAI based unsupervised knowledge discovering for Operation modes in a WWTP. A real case: AQUAVALL WWTP—0
AI Readiness in Healthcare through Storytelling XAI—0
Assessing the Efficacy of Deep Learning Approaches for Facial Expression Recognition in Individuals with Intellectual Disabilities—0
EVolutionary Independent DEtermiNistiC Explanation—0
Explainability for identification of vulnerable groups in machine learning models—0
Carefully choose the baseline: Lessons learned from applying XAI attribution methods for regression tasks in geoscience—0
AI Approaches in Processing and Using Data in Personalized Medicine—0
A Critical Review of Inductive Logic Programming Techniques for Explainable AI—0
Can Requirements Engineering Support Explainable Artificial Intelligence? Towards a User-Centric Approach for Explainability Requirements—0
Can Explainable AI Explain Unfairness? A Framework for Evaluating Explainable AI—0
Applications of Explainable artificial intelligence in Earth system science—0
Evaluating explainable artificial intelligence methods for multi-label deep learning classification tasks in remote sensing—0
CAManim: Animating end-to-end network activation maps—0
A Peek Into the Reasoning of Neural Networks: Interpreting with Structural Visual Concepts—0
Anytime Approximate Formal Feature Attribution—0
A Hypothesis on Good Practices for AI-based Systems for Financial Time Series Forecasting: Towards Domain-Driven XAI Methods—0
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