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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 741750 of 971 papers

TitleStatusHype
Interpretable Data-Based Explanations for Fairness Debugging0
Interpretable Medical Imagery Diagnosis with Self-Attentive Transformers: A Review of Explainable AI for Health Care0
Interpreting convolutional networks trained on textual data0
Interpreting Outliers in Time Series Data through Decoding Autoencoder0
Introducing δ-XAI: a novel sensitivity-based method for local AI explanations0
iSAGE: An Incremental Version of SAGE for Online Explanation on Data Streams0
IsoEx: an explainable unsupervised approach to process event logs cyber investigation0
Isopignistic Canonical Decomposition via Belief Evolution Network0
IXAII: An Interactive Explainable Artificial Intelligence Interface for Decision Support Systems0
Justifiable Artificial Intelligence: Engineering Large Language Models for Legal Applications0
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