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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 871880 of 971 papers

TitleStatusHype
Why model why? Assessing the strengths and limitations of LIMECode0
Explainable Incipient Fault Detection Systems for Photovoltaic Panels0
Data Representing Ground-Truth Explanations to Evaluate XAI Methods0
Qualitative Investigation in Explainable Artificial Intelligence: A Bit More Insight from Social Science0
FairLens: Auditing Black-box Clinical Decision Support Systems0
Explainable AI meets Healthcare: A Study on Heart Disease Dataset0
Explainable Artificial Intelligence Recommendation System by Leveraging the Semantics of Adverse Childhood Experiences: Proof-of-Concept Prototype Development0
Causal Shapley Values: Exploiting Causal Knowledge to Explain Individual Predictions of Complex ModelsCode0
Towards Harnessing Natural Language Generation to Explain Black-box Models0
ExTRA: Explainable Therapy-Related Annotations0
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