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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 851–900 of 971 papers

TitleStatusHype
What Do We Want From Explainable Artificial Intelligence (XAI)? -- A Stakeholder Perspective on XAI and a Conceptual Model Guiding Interdisciplinary XAI Research—0
VitrAI -- Applying Explainable AI in the Real World—0
Principles of Explanation in Human-AI Systems—0
Mitigating belief projection in explainable artificial intelligence via Bayesian TeachingCode0
Achieving Explainability for Plant Disease Classification with Disentangled Variational Autoencoders—0
Convolutional Neural Network Interpretability with General Pattern Theory—0
Unbox the Black-box for the Medical Explainable AI via Multi-modal and Multi-centre Data Fusion: A Mini-Review, Two Showcases and Beyond—0
A Survey on Understanding, Visualizations, and Explanation of Deep Neural Networks—0
Hierarchical Variational Autoencoder for Visual Counterfactuals—0
Diagnosis of Acute Poisoning Using Explainable Artificial Intelligence—0
Matching Representations of Explainable Artificial Intelligence and Eye Gaze for Human-Machine Interaction—0
Visual explanation of black-box model: Similarity Difference and Uniqueness (SIDU) methodCode0
Explainable Artificial Intelligence Approaches: A Survey—0
Explainability of deep vision-based autonomous driving systems: Review and challenges—0
Explainable Artificial Intelligence (XAI): An Engineering Perspective—0
Deep Unsupervised Identification of Selected SNPs between Adapted Populations on Pool-seq Data—0
XAI-P-T: A Brief Review of Explainable Artificial Intelligence from Practice to TheoryCode0
Checklist for responsible deep learning modeling of medical images based on COVID-19 detection studiesCode0
Shapley values for cluster importance: How clusters of the training data affect a prediction—0
Interpretability and Explainability: A Machine Learning Zoo Mini-tour—0
Why model why? Assessing the strengths and limitations of LIMECode0
Explainable Incipient Fault Detection Systems for Photovoltaic Panels—0
Data Representing Ground-Truth Explanations to Evaluate XAI Methods—0
Qualitative Investigation in Explainable Artificial Intelligence: A Bit More Insight from Social Science—0
FairLens: Auditing Black-box Clinical Decision Support Systems—0
Explainable AI meets Healthcare: A Study on Heart Disease Dataset—0
Explainable Artificial Intelligence Recommendation System by Leveraging the Semantics of Adverse Childhood Experiences: Proof-of-Concept Prototype Development—0
Causal Shapley Values: Exploiting Causal Knowledge to Explain Individual Predictions of Complex ModelsCode0
Towards Harnessing Natural Language Generation to Explain Black-box Models—0
ExTRA: Explainable Therapy-Related Annotations—0
Argumentation Theoretical Frameworks for Explainable Artificial Intelligence—0
Explainable Artificial Intelligence for Manufacturing Cost Estimation and Machining Feature VisualizationCode0
An Experimentation Platform for Explainable Coalition Situational Understanding—0
Interpreting convolutional networks trained on textual data—0
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
Local Post-Hoc Explanations for Predictive Process Monitoring in Manufacturing—0
What Do You See? Evaluation of Explainable Artificial Intelligence (XAI) Interpretability through Neural Backdoors—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
Should We Trust (X)AI? Design Dimensions for Structured Experimental Evaluations—0
An Argumentation-based Approach for Explaining Goal Selection in Intelligent Agents—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
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