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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 221230 of 971 papers

TitleStatusHype
GECOBench: A Gender-Controlled Text Dataset and Benchmark for Quantifying Biases in ExplanationsCode0
Explainable Artificial Intelligence and Multicollinearity : A Mini Review of Current ApproachesCode0
Incorporating uncertainty quantification into travel mode choice modeling: a Bayesian neural network (BNN) approach and an uncertainty-guided active survey framework0
Explainable AI for Comparative Analysis of Intrusion Detection ModelsCode0
Applications of Explainable artificial intelligence in Earth system science0
CoXQL: A Dataset for Parsing Explanation Requests in Conversational XAI SystemsCode0
Evolutionary Computation and Explainable AI: A Roadmap to Understandable Intelligent Systems0
Are Large Language Models the New Interface for Data Pipelines?0
GPU-Accelerated Rule Evaluation and Evolution0
nn2poly: An R Package for Converting Neural Networks into Interpretable Polynomials0
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