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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 641650 of 971 papers

TitleStatusHype
Alterfactual Explanations -- The Relevance of Irrelevance for Explaining AI Systems0
Creating an Explainable Intrusion Detection System Using Self Organizing Maps0
BASED-XAI: Breaking Ablation Studies Down for Explainable Artificial IntelligenceCode1
Data integration in systems genetics and aging research0
Evaluating Human-like Explanations for Robot Actions in Reinforcement Learning Scenarios0
SESS: Saliency Enhancing with Scaling and SlidingCode0
Explainability in Deep Reinforcement Learning, a Review into Current Methods and Applications0
NN2Rules: Extracting Rule List from Neural NetworksCode0
TE2Rules: Explaining Tree Ensembles using RulesCode1
Explaining Any ML Model? -- On Goals and Capabilities of XAI0
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