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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 941950 of 971 papers

TitleStatusHype
Towards Best Practice in Explaining Neural Network Decisions with LRPCode0
Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AICode0
Meaningful Data Sampling for a Faithful Local Explanation MethodCode0
Do Explanations Reflect Decisions? A Machine-centric Strategy to Quantify the Performance of Explainability Algorithms0
How a minimal learning agent can infer the existence of unobserved variables in a complex environment0
Towards Explainable Artificial Intelligence0
On Evaluating Explainability Algorithms0
Towards Explainable Neural-Symbolic Visual Reasoning0
Towards a Rigorous Evaluation of XAI Methods on Time Series0
The Challenge of Imputation in Explainable Artificial Intelligence Models0
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