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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 176–200 of 971 papers

TitleStatusHype
Classification and Explanation of Distributed Denial-of-Service (DDoS) Attack Detection using Machine Learning and Shapley Additive Explanation (SHAP) Methods—0
Classification of Viral Pneumonia X-ray Images with the Aucmedi Framework—0
Classifying Simulated Gait Impairments using Privacy-preserving Explainable Artificial Intelligence and Mobile Phone Videos—0
A Review on Explainable Artificial Intelligence for Healthcare: Why, How, and When?—0
CNN-based explanation ensembling for dataset, representation and explanations evaluation—0
Challenges and Opportunities in Text Generation Explainability—0
Deciphering knee osteoarthritis diagnostic features with explainable artificial intelligence: A systematic review—0
A Lightweight IDS for Early APT Detection Using a Novel Feature Selection Method—0
A Data-Driven Exploration of Elevation Cues in HRTFs: An Explainable AI Perspective Across Multiple Datasets—0
A Perspective on Explainable Artificial Intelligence Methods: SHAP and LIME—0
ARTxAI: Explainable Artificial Intelligence Curates Deep Representation Learning for Artistic Images using Fuzzy Techniques—0
Comparative Analysis of Hyperspectral Image Reconstruction Using Deep Learning for Agricultural and Biological Applications—0
Comparing interpretation methods in mental state decoding analyses with deep learning models—0
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
DCNFIS: Deep Convolutional Neuro-Fuzzy Inference System—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
Asset Pricing and Deep Learning—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
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