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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 601610 of 971 papers

TitleStatusHype
Towards the Linear Algebra Based Taxonomy of XAI Explanations0
ChatGPT or Human? Detect and Explain. Explaining Decisions of Machine Learning Model for Detecting Short ChatGPT-generated Text0
Explainable AI does not provide the explanations end-users are asking for0
CEnt: An Entropy-based Model-agnostic Explainability Framework to Contrast Classifiers' DecisionsCode0
An Artificial Intelligence-based model for cell killing prediction: development, validation and explainability analysis of the ANAKIN model0
MAFUS: a Framework to predict mortality risk in MAFLD subjects0
Explainable Data Poison Attacks on Human Emotion Evaluation Systems based on EEG SignalsCode0
Against Algorithmic Exploitation of Human Vulnerabilities0
Explaining Imitation Learning through Frames0
Hierarchical Explanations for Video Action RecognitionCode0
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