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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 361370 of 971 papers

TitleStatusHype
Energy-based Model for Accurate Shapley Value Estimation in Interpretable Deep Learning Predictive ModelingCode0
Automatic explanation of the classification of Spanish legal judgments in jurisdiction-dependent law categories with tree estimators0
Leveraging Counterfactual Paths for Contrastive Explanations of POMDP Policies0
Leveraging Expert Input for Robust and Explainable AI-Assisted Lung Cancer Detection in Chest X-rays0
Intrinsic Subgraph Generation for Interpretable Graph based Visual Question AnsweringCode0
Clinical Domain Knowledge-Derived Template Improves Post Hoc AI Explanations in Pneumothorax ClassificationCode0
Revealing Vulnerabilities of Neural Networks in Parameter Learning and Defense Against Explanation-Aware Backdoors0
Enhancing UAV Security Through Zero Trust Architecture: An Advanced Deep Learning and Explainable AI Analysis0
The Anatomy of Adversarial Attacks: Concept-based XAI Dissection0
The Limits of Perception: Analyzing Inconsistencies in Saliency Maps in XAI0
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