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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

Showing 376–400 of 971 papers

TitleStatusHype
Analysis and Evaluation of Explainable Artificial Intelligence on Suicide Risk Assessment—0
A Complete Characterisation of ReLU-Invariant Distributions—0
Counterfactual and Semifactual Explanations in Abstract Argumentation: Formal Foundations, Complexity and Computation—0
Correlation between morphological evolution of splashing drop and exerted impact force revealed by interpretation of explainable artificial intelligence—0
A Survey of Explainable Knowledge Tracing—0
Regularizing Explanations in Bayesian Convolutional Neural Networks—0
Convolutional Neural Network Interpretability with General Pattern Theory—0
A Survey of Explainable Artificial Intelligence (XAI) in Financial Time Series Forecasting—0
Contextual Trust—0
A Survey of Explainable AI and Proposal for a Discipline of Explanation Engineering—0
Explainable Artificial Intelligence Model for Evaluating Shear Strength Parameters of Municipal Solid Waste Across Diverse Compositional Profiles—0
A Survey of Accessible Explainable Artificial Intelligence Research—0
A Multi-Modal Explainability Approach for Human-Aware Robots in Multi-Party Conversation—0
Explainable artificial intelligence in breast cancer detection and risk prediction: A systematic scoping review—0
Explainable Artificial Intelligence in Biomedical Image Analysis: A Comprehensive Survey—0
Explainable Artificial Intelligence in Retinal Imaging for the detection of Systemic Diseases—0
Concept Induction using LLMs: a user experiment for assessment—0
Asset Pricing and Deep Learning—0
Explainable Artificial Intelligence in Construction: The Content, Context, Process, Outcome Evaluation Framework—0
Enabling Verification of Deep Neural Networks in Perception Tasks Using Fuzzy Logic and Concept Embeddings—0
Shapley values for cluster importance: How clusters of the training data affect a prediction—0
Explainable Artificial Intelligence for Smart City Application: A Secure and Trusted Platform—0
Explainable Artificial Intelligence Methods in Combating Pandemics: A Systematic Review—0
Concept Embedding Analysis: A Review—0
Assessing high-order effects in feature importance via predictability decomposition—0
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