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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 251260 of 971 papers

TitleStatusHype
ExplainableDetector: Exploring Transformer-based Language Modeling Approach for SMS Spam Detection with Explainability Analysis0
LLMs for XAI: Future Directions for Explaining Explanations0
Relevant Irrelevance: Generating Alterfactual Explanations for Image ClassifiersCode0
Explanation as a Watermark: Towards Harmless and Multi-bit Model Ownership Verification via Watermarking Feature AttributionCode1
Counterfactual and Semifactual Explanations in Abstract Argumentation: Formal Foundations, Complexity and Computation0
False Sense of Security in Explainable Artificial Intelligence (XAI)0
Explainable Interface for Human-Autonomy Teaming: A Survey0
Isopignistic Canonical Decomposition via Belief Evolution Network0
A Fresh Look at Sanity Checks for Saliency MapsCode1
Explainable Multi-Label Classification of MBTI Types0
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