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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 2130 of 971 papers

TitleStatusHype
Explanation User Interfaces: A Systematic Literature Review0
From What Ifs to Insights: Counterfactuals in Causal Inference vs. Explainable AI0
Addressing the Scarcity of Benchmarks for Graph XAICode0
WebXAII: an open-source web framework to study human-XAI interaction0
Heart2Mind: Human-Centered Contestable Psychiatric Disorder Diagnosis System using Wearable ECG MonitorsCode0
Most General Explanations of Tree Ensembles (Extended Version)0
Financial Fraud Detection Using Explainable AI and Stacking Ensemble Methods0
PnPXAI: A Universal XAI Framework Providing Automatic Explanations Across Diverse Modalities and ModelsCode2
SIM-Shapley: A Stable and Computationally Efficient Approach to Shapley Value ApproximationCode0
Tuning for Trustworthiness -- Balancing Performance and Explanation Consistency in Neural Network Optimization0
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