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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 3140 of 971 papers

TitleStatusHype
Explainable Artificial Intelligence Techniques for Software Development Lifecycle: A Phase-specific Survey0
Discovering Concept Directions from Diffusion-based Counterfactuals via Latent Clustering0
Privacy Risks and Preservation Methods in Explainable Artificial Intelligence: A Scoping Review0
Multimodal Doctor-in-the-Loop: A Clinically-Guided Explainable Framework for Predicting Pathological Response in Non-Small Cell Lung Cancer0
Machine Learning Meets Transparency in Osteoporosis Risk Assessment: A Comparative Study of ML and Explainability Analysis0
Explainable AI in Spatial AnalysisCode2
XBreaking: Explainable Artificial Intelligence for Jailbreaking LLMs0
DiCE-Extended: A Robust Approach to Counterfactual Explanations in Machine Learning0
ApproXAI: Energy-Efficient Hardware Acceleration of Explainable AI using Approximate Computing0
Generative and Explainable AI for High-Dimensional Channel EstimationCode0
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