SOTAVerified

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

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Showing 676–700 of 971 papers

TitleStatusHype
A Complete Characterisation of ReLU-Invariant Distributions—0
A Comprehensive Study on Medical Image Segmentation using Deep Neural Networks—0
A Context-Sensitive Approach to XAI in Music Performance—0
A Critical Review of Inductive Logic Programming Techniques for Explainable AI—0
Adapting the Biological SSVEP Response to Artificial Neural Networks—0
A Data-Driven Exploration of Elevation Cues in HRTFs: An Explainable AI Perspective Across Multiple Datasets—0
A Data-Driven Framework for Identifying Investment Opportunities in Private Equity—0
Additive-feature-attribution methods: a review on explainable artificial intelligence for fluid dynamics and heat transfer—0
A Deep Generative XAI Framework for Natural Language Inference Explanations Generation—0
Adherence and Constancy in LIME-RS Explanations for Recommendation—0
Advancing Nearest Neighbor Explanation-by-Example with Critical Classification Regions—0
Adversarial Attack for Explanation Robustness of Rationalization Models—0
Against Algorithmic Exploitation of Human Vulnerabilities—0
Exploiting auto-encoders and segmentation methods for middle-level explanations of image classification systems—0
A general approach to compute the relevance of middle-level input features—0
A Grounded Interaction Protocol for Explainable Artificial Intelligence—0
AgroXAI: Explainable AI-Driven Crop Recommendation System for Agriculture 4.0—0
A Hypothesis on Good Practices for AI-based Systems for Financial Time Series Forecasting: Towards Domain-Driven XAI Methods—0
AI Approaches in Processing and Using Data in Personalized Medicine—0
AI Readiness in Healthcare through Storytelling XAI—0
A Lightweight IDS for Early APT Detection Using a Novel Feature Selection Method—0
Alterfactual Explanations -- The Relevance of Irrelevance for Explaining AI Systems—0
A Means-End Account of Explainable Artificial Intelligence—0
A Meta-Analysis of the Utility of Explainable Artificial Intelligence in Human-AI Decision-Making—0
A Meta Survey of Quality Evaluation Criteria in Explanation Methods—0
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