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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 676–700 of 971 papers

TitleStatusHype
A Means-End Account of Explainable Artificial Intelligence—0
Multi-modal volumetric concept activation to explain detection and classification of metastatic prostate cancer on PSMA-PET/CTCode0
Diagnosis of Paratuberculosis in Histopathological Images Based on Explainable Artificial Intelligence and Deep Learning—0
An Urban Population Health Observatory for Disease Causal Pathway Analysis and Decision Support: Underlying Explainable Artificial Intelligence Model—0
From Interpretable Filters to Predictions of Convolutional Neural Networks with Explainable Artificial Intelligence—0
AI Approaches in Processing and Using Data in Personalized Medicine—0
Alterfactual Explanations -- The Relevance of Irrelevance for Explaining AI Systems—0
Creating an Explainable Intrusion Detection System Using Self Organizing Maps—0
Data integration in systems genetics and aging research—0
Evaluating Human-like Explanations for Robot Actions in Reinforcement Learning Scenarios—0
SESS: Saliency Enhancing with Scaling and SlidingCode0
Explainability in Deep Reinforcement Learning, a Review into Current Methods and Applications—0
NN2Rules: Extracting Rule List from Neural NetworksCode0
Explaining Any ML Model? -- On Goals and Capabilities of XAI—0
"Explanation" is Not a Technical Term: The Problem of Ambiguity in XAI—0
Explanation-based Counterfactual Retraining(XCR): A Calibration Method for Black-box Models—0
Stop ordering machine learning algorithms by their explainability! A user-centered investigation of performance and explainability—0
Eliminating The Impossible, Whatever Remains Must Be TrueCode0
Visualizing and Understanding Contrastive LearningCode0
Machine Learning in Sports: A Case Study on Using Explainable Models for Predicting Outcomes of Volleyball Matches—0
Explainable expected goal models for performance analysis in football analyticsCode0
Attributions Beyond Neural Networks: The Linear Program Case—0
Mediators: Conversational Agents Explaining NLP Model Behavior—0
ECLAD: Extracting Concepts with Local Aggregated Descriptors—0
Challenges in Applying Explainability Methods to Improve the Fairness of NLP Models—0
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