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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 876–900 of 971 papers

TitleStatusHype
Squashing activation functions in benchmark tests: towards eXplainable Artificial Intelligence using continuous-valued logic—0
A general approach to compute the relevance of middle-level input features—0
A Series of Unfortunate Counterfactual Events: the Role of Time in Counterfactual Explanations—0
Integrating Intrinsic and Extrinsic Explainability: The Relevance of Understanding Neural Networks for Human-Robot Interaction—0
Explainability via Responsibility—0
Ensembles of Convolutional Neural Networks models for pediatric pneumonia diagnosis—0
The design and implementation of Language Learning Chatbot with XAI using Ontology and Transfer Learning—0
Landscape of R packages for eXplainable Artificial IntelligenceCode1
What Do You See? Evaluation of Explainable Artificial Intelligence (XAI) Interpretability through Neural Backdoors—0
Local Post-Hoc Explanations for Predictive Process Monitoring in Manufacturing—0
Impact of lung segmentation on the diagnosis and explanation of COVID-19 in chest X-ray imagesCode0
Better Model Selection with a new Definition of Feature Importance—0
SCOUTER: Slot Attention-based Classifier for Explainable Image RecognitionCode1
An Argumentation-based Approach for Explaining Goal Selection in Intelligent Agents—0
Should We Trust (X)AI? Design Dimensions for Structured Experimental Evaluations—0
Argumentation-based Agents that Explain their Decisions—0
TripleTree: A Versatile Interpretable Representation of Black Box Agents and their EnvironmentsCode0
Quantifying Explainability of Saliency Methods in Deep Neural Networks with a Synthetic DatasetCode0
Explainable Artificial Intelligence for Process Mining: A General Overview and Application of a Novel Local Explanation Approach for Predictive Process Monitoring—0
XNAP: Making LSTM-based Next Activity Predictions Explainable by Using LRPCode0
Explainability in Deep Reinforcement Learning—0
Survey of XAI in digital pathology—0
Explainable Artificial Intelligence Based Fault Diagnosis and Insight Harvesting for Steel Plates Manufacturing—0
Distributed Linguistic Representations in Decision Making: Taxonomy, Key Elements and Applications, and Challenges in Data Science and Explainable Artificial Intelligence—0
Safety design concepts for statistical machine learning components toward accordance with functional safety standards—0
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