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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 76–100 of 971 papers

TitleStatusHype
Explainable Deep Learning Methods in Medical Image Classification: A SurveyCode1
Explaining black boxes with a SMILE: Statistical Model-agnostic Interpretability with Local ExplanationsCode1
Explaining Black-Box Models through CounterfactualsCode1
Finding Alignments Between Interpretable Causal Variables and Distributed Neural RepresentationsCode1
From Attribution Maps to Human-Understandable Explanations through Concept Relevance PropagationCode1
TE2Rules: Explaining Tree Ensembles using RulesCode1
Gaussian Process Regression With Interpretable Sample-Wise Feature WeightsCode1
Proposed Guidelines for the Responsible Use of Explainable Machine LearningCode1
In-Context Explainers: Harnessing LLMs for Explaining Black Box ModelsCode1
AudioMNIST: Exploring Explainable Artificial Intelligence for Audio Analysis on a Simple BenchmarkCode1
Landscape of R packages for eXplainable Artificial IntelligenceCode1
Logic Explained NetworksCode1
Medical Slice Transformer: Improved Diagnosis and Explainability on 3D Medical Images with DINOv2Code1
MICA: Towards Explainable Skin Lesion Diagnosis via Multi-Level Image-Concept AlignmentCode1
Model-contrastive explanations through symbolic reasoningCode1
An Explainable AI Framework for Artificial Intelligence of Medical Things—0
An Experimentation Platform for Explainable Coalition Situational Understanding—0
Adversarial Attack for Explanation Robustness of Rationalization Models—0
A New Perspective on Evaluation Methods for Explainable Artificial Intelligence (XAI)—0
Abstraction, Validation, and Generalization for Explainable Artificial Intelligence—0
A Unified Framework for Evaluating the Effectiveness and Enhancing the Transparency of Explainable AI Methods in Real-World Applications—0
A New Deep Learning and XAI-Based Algorithm for Features Selection in Genomics—0
Advancing Nearest Neighbor Explanation-by-Example with Critical Classification Regions—0
An Artificial Intelligence-based model for cell killing prediction: development, validation and explainability analysis of the ANAKIN model—0
Adherence and Constancy in LIME-RS Explanations for Recommendation—0
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