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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 701–725 of 971 papers

TitleStatusHype
HaT5: Hate Language Identification using Text-to-Text Transfer Transformer—0
Helpful, Misleading or Confusing: How Humans Perceive Fundamental Building Blocks of Artificial Intelligence Explanations—0
Hierarchical Variational Autoencoder for Visual Counterfactuals—0
Towards Explainable Neural-Symbolic Visual Reasoning—0
How a minimal learning agent can infer the existence of unobserved variables in a complex environment—0
How Deep is Your Art: An Experimental Study on the Limits of Artistic Understanding in a Single-Task, Single-Modality Neural Network—0
How Human-Centered Explainable AI Interface Are Designed and Evaluated: A Systematic Survey—0
How much informative is your XAI? A decision-making assessment task to objectively measure the goodness of explanations—0
How Reliable and Stable are Explanations of XAI Methods?—0
How should AI decisions be explained? Requirements for Explanations from the Perspective of European Law—0
Human Attention-Guided Explainable Artificial Intelligence for Computer Vision Models—0
Human-Centric Artificial Intelligence Architecture for Industry 5.0 Applications—0
Human in the AI loop via xAI and Active Learning for Visual Inspection—0
Identifying contributors to supply chain outcomes in a multi-echelon setting: a decentralised approach—0
Identifying Dominant Industrial Sectors in Market States of the S&P 500 Financial Data—0
Identifying Student Profiles Within Online Judge Systems Using Explainable Artificial Intelligence—0
ILLC: Iterative Layer-by-Layer Compression for Enhancing Structural Faithfulness in SpArX—0
Impact Of Explainable AI On Cognitive Load: Insights From An Empirical Study—0
Impact of Feature Encoding on Malware Classification Explainability—0
Implementing local-explainability in Gradient Boosting Trees: Feature Contribution—0
Improved Explainability of Capsule Networks: Relevance Path by Agreement—0
Improved Explanatory Efficacy on Human Affect and Workload through Interactive Process in Artificial Intelligence—0
Improvement of a Prediction Model for Heart Failure Survival through Explainable Artificial Intelligence—0
Improving deep learning with prior knowledge and cognitive models: A survey on enhancing explainability, adversarial robustness and zero-shot learning—0
Incorporating uncertainty quantification into travel mode choice modeling: a Bayesian neural network (BNN) approach and an uncertainty-guided active survey framework—0
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