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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 626–650 of 971 papers

TitleStatusHype
Shapelet-Based Counterfactual Explanations for Multivariate Time SeriesCode0
ProtoPFormer: Concentrating on Prototypical Parts in Vision Transformers for Interpretable Image RecognitionCode1
Carefully choose the baseline: Lessons learned from applying XAI attribution methods for regression tasks in geoscience—0
Causality-Inspired Taxonomy for Explainable Artificial Intelligence—0
Transcending XAI Algorithm Boundaries through End-User-Inspired Design—0
Explainable Reinforcement Learning on Financial Stock Trading using SHAP—0
Explainable Artificial Intelligence for Assault Sentence Prediction in New Zealand—0
An Empirical Comparison of Explainable Artificial Intelligence Methods for Clinical Data: A Case Study on Traumatic Brain InjuryCode0
Trustworthy Visual Analytics in Clinical Gait Analysis: A Case Study for Patients with Cerebral Palsy—0
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
AI Approaches in Processing and Using Data in Personalized Medicine—0
From Interpretable Filters to Predictions of Convolutional Neural Networks with Explainable Artificial Intelligence—0
Alterfactual Explanations -- The Relevance of Irrelevance for Explaining AI Systems—0
Creating an Explainable Intrusion Detection System Using Self Organizing Maps—0
BASED-XAI: Breaking Ablation Studies Down for Explainable Artificial IntelligenceCode1
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
TE2Rules: Explaining Tree Ensembles using RulesCode1
Explaining Any ML Model? -- On Goals and Capabilities of XAI—0
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