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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 191200 of 971 papers

TitleStatusHype
DCNFIS: Deep Convolutional Neuro-Fuzzy Inference System0
Concept-Attention Whitening for Interpretable Skin Lesion Diagnosis0
Concept-based Explainable Artificial Intelligence: A Survey0
Concept-Based Explainable Artificial Intelligence: Metrics and Benchmarks0
Concept Embedding Analysis: A Review0
Enabling Verification of Deep Neural Networks in Perception Tasks Using Fuzzy Logic and Concept Embeddings0
Concept Induction using LLMs: a user experiment for assessment0
Asset Pricing and Deep Learning0
A Practical guide on Explainable AI Techniques applied on Biomedical use case applications0
Causal versus Marginal Shapley Values for Robotic Lever Manipulation Controlled using Deep Reinforcement Learning0
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