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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 271280 of 971 papers

TitleStatusHype
Evaluating the Stability of Semantic Concept Representations in CNNs for Robust Explainability0
CAManim: Animating end-to-end network activation maps0
A Peek Into the Reasoning of Neural Networks: Interpreting with Structural Visual Concepts0
Anytime Approximate Formal Feature Attribution0
A Hypothesis on Good Practices for AI-based Systems for Financial Time Series Forecasting: Towards Domain-Driven XAI Methods0
Evaluation of Human-Understandability of Global Model Explanations using Decision Tree0
CACTUS: a Comprehensive Abstraction and Classification Tool for Uncovering Structures0
Multihop: Leveraging Complex Models to Learn Accurate Simple Models0
An Urban Population Health Observatory for Disease Causal Pathway Analysis and Decision Support: Underlying Explainable Artificial Intelligence Model0
BSED: Baseline Shapley-Based Explainable Detector0
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