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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

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Showing 276–300 of 971 papers

TitleStatusHype
Introducing δ-XAI: a novel sensitivity-based method for local AI explanations—0
Automated Explanation Selection for Scientific Discovery—0
Explainable Artificial Intelligence Techniques for Irregular Temporal Classification of Multidrug Resistance Acquisition in Intensive Care Unit Patients—0
A Survey of Explainable Artificial Intelligence (XAI) in Financial Time Series Forecasting—0
Explainable AI-based Intrusion Detection System for Industry 5.0: An Overview of the Literature, associated Challenges, the existing Solutions, and Potential Research Directions—0
XAI meets LLMs: A Survey of the Relation between Explainable AI and Large Language Models—0
An Explainable Fast Deep Neural Network for Emotion Recognition—0
Geometric Remove-and-Retrain (GOAR): Coordinate-Invariant eXplainable AI Assessment—0
End-to-end Stroke imaging analysis, using reservoir computing-based effective connectivity, and interpretable Artificial intelligenceCode0
Are Linear Regression Models White Box and Interpretable?—0
XEQ Scale for Evaluating XAI Experience Quality—0
Robustness of Explainable Artificial Intelligence in Industrial Process Modelling—0
Explainable artificial intelligence in breast cancer detection and risk prediction: A systematic scoping review—0
Robust and Explainable Framework to Address Data Scarcity in Diagnostic Imaging—0
Impact of satellites streaks for observational astronomy: a study on data captured during one year from Luxembourg Greater RegionCode0
Explainable Image Recognition via Enhanced Slot-attention Based Classifier—0
From Data to Commonsense Reasoning: The Use of Large Language Models for Explainable AI—0
How Reliable and Stable are Explanations of XAI Methods?—0
A Survey of Accessible Explainable Artificial Intelligence Research—0
Explainability of Machine Learning Models under Missing DataCode0
ShapG: new feature importance method based on the Shapley valueCode0
FreqRISE: Explaining time series using frequency maskingCode0
GFM4MPM: Towards Geospatial Foundation Models for Mineral Prospectivity Mapping—0
MiSuRe is all you need to explain your image segmentation—0
Explainable Artificial Intelligence and Multicollinearity : A Mini Review of Current ApproachesCode0
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