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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 726–750 of 971 papers

TitleStatusHype
Enabling Verification of Deep Neural Networks in Perception Tasks Using Fuzzy Logic and Concept Embeddings—0
A Critical Review of Inductive Logic Programming Techniques for Explainable AI—0
Improving Deep Neural Network Classification Confidence using Heatmap-based eXplainable AICode0
Towards a Shapley Value Graph Framework for Medical peer-influence—0
Explainability Is in the Mind of the Beholder: Establishing the Foundations of Explainable Artificial Intelligence—0
Explainable Artificial Intelligence for Pharmacovigilance: What Features Are Important When Predicting Adverse Outcomes?—0
Towards Understanding Human Functional Brain Development with Explainable Artificial Intelligence: Challenges and Perspectives—0
Explainable Artificial Intelligence Methods in Combating Pandemics: A Systematic Review—0
Explainable Artificial Intelligence for Autonomous Driving: A Comprehensive Overview and Field Guide for Future Research Directions—0
Interpretable Data-Based Explanations for Fairness Debugging—0
Utilizing XAI technique to improve autoencoder based model for computer network anomaly detection with shapley additive explanation(SHAP)—0
Analyzing and Improving the Robustness of Tabular Classifiers using Counterfactual ExplanationsCode0
A Complete Characterisation of ReLU-Invariant Distributions—0
ContrXT: Generating Contrastive Explanations from any Text ClassifierCode1
Gaussian Process Regression With Interpretable Sample-Wise Feature WeightsCode1
Evaluating saliency methods on artificial data with different background typesCode0
Decoding the Protein-ligand Interactions Using Parallel Graph Neural NetworksCode0
Evaluation of Interpretability for Deep Learning algorithms in EEG Emotion Recognition: A case study in AutismCode1
Explainable Deep Image Classifiers for Skin Lesion Diagnosis—0
A Deep Generative XAI Framework for Natural Language Inference Explanations Generation—0
A Practical guide on Explainable AI Techniques applied on Biomedical use case applications—0
Causal versus Marginal Shapley Values for Robotic Lever Manipulation Controlled using Deep Reinforcement Learning—0
Exploring Explainable AI in the Financial Sector: Perspectives of Banks and Supervisory Authorities—0
Explainable Artificial Intelligence for Smart City Application: A Secure and Trusted Platform—0
Counterfactual Shapley Additive ExplanationsCode1
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