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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 801–825 of 971 papers

TitleStatusHype
Comprehensible Artificial Intelligence on Knowledge Graphs: A survey—0
Computational identification of ketone metabolism as a key regulator of sleep stability and circadian dynamics via real-time metabolic profiling—0
Concept-Attention Whitening for Interpretable Skin Lesion Diagnosis—0
Concept-based Explainable Artificial Intelligence: A Survey—0
Concept-Based Explainable Artificial Intelligence: Metrics and Benchmarks—0
Concept Embedding Analysis: A Review—0
Enabling Verification of Deep Neural Networks in Perception Tasks Using Fuzzy Logic and Concept Embeddings—0
Concept Induction using LLMs: a user experiment for assessment—0
Contextual Trust—0
Convolutional Neural Network Interpretability with General Pattern Theory—0
Regularizing Explanations in Bayesian Convolutional Neural Networks—0
Correlation between morphological evolution of splashing drop and exerted impact force revealed by interpretation of explainable artificial intelligence—0
Counterfactual and Semifactual Explanations in Abstract Argumentation: Formal Foundations, Complexity and Computation—0
Counterfactual Explanations for Clustering Models—0
Counterfactual Explanations of Black-box Machine Learning Models using Causal Discovery with Applications to Credit Rating—0
Counterfactual Formulation of Patient-Specific Root Causes of Disease—0
Counterfactuals and Causability in Explainable Artificial Intelligence: Theory, Algorithms, and Applications—0
Creating an Explainable Intrusion Detection System Using Self Organizing Maps—0
Crown-CAM: Interpretable Visual Explanations for Tree Crown Detection in Aerial Images—0
DA-DGCEx: Ensuring Validity of Deep Guided Counterfactual Explanations With Distribution-Aware Autoencoder Loss—0
Data integration in systems genetics and aging research—0
Data Representing Ground-Truth Explanations to Evaluate XAI Methods—0
Dataset | Mindset = Explainable AI | Interpretable AI—0
DCNFIS: Deep Convolutional Neuro-Fuzzy Inference System—0
Deciphering AutoML Ensembles: cattleia's Assistance in Decision-Making—0
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