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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 561570 of 971 papers

TitleStatusHype
Survey on Deep Fuzzy Systems in regression applications: a view on interpretability0
Sustainable Personalisation and Explainability in Dyadic Data Systems0
Symbolic Knowledge Extraction and Injection with Sub-symbolic Predictors: A Systematic Literature Review0
TbExplain: A Text-based Explanation Method for Scene Classification Models with the Statistical Prediction Correction0
Tell me more: Intent Fulfilment Framework for Enhancing User Experiences in Conversational XAI0
T-Explainer: A Model-Agnostic Explainability Framework Based on Gradients0
The Anatomy of Adversarial Attacks: Concept-based XAI Dissection0
The Challenge of Imputation in Explainable Artificial Intelligence Models0
The Conflict Between Explainable and Accountable Decision-Making Algorithms0
The Dark Side of Explanations: Poisoning Recommender Systems with Counterfactual Examples0
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