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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 321330 of 971 papers

TitleStatusHype
Explainability is NOT a Game0
Case-based Explainability for Random Forest: Prototypes, Critics, Counter-factuals and Semi-factuals0
CAT: Concept-level backdoor ATtacks for Concept Bottleneck Models0
Explainability of deep vision-based autonomous driving systems: Review and challenges0
Explainability through uncertainty: Trustworthy decision-making with neural networks0
Explainability via Responsibility0
Explainable Activity Recognition for Smart Home Systems0
Explainable AI-Based Interface System for Weather Forecasting Model0
Explainable AI-based Intrusion Detection System for Industry 5.0: An Overview of the Literature, associated Challenges, the existing Solutions, and Potential Research Directions0
Enhancing Counterfactual Image Generation Using Mahalanobis Distance with Distribution Preferences in Feature Space0
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