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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 601–625 of 971 papers

TitleStatusHype
Towards the Linear Algebra Based Taxonomy of XAI Explanations—0
ChatGPT or Human? Detect and Explain. Explaining Decisions of Machine Learning Model for Detecting Short ChatGPT-generated Text—0
Explainable AI does not provide the explanations end-users are asking for—0
CEnt: An Entropy-based Model-agnostic Explainability Framework to Contrast Classifiers' DecisionsCode0
An Artificial Intelligence-based model for cell killing prediction: development, validation and explainability analysis of the ANAKIN model—0
MAFUS: a Framework to predict mortality risk in MAFLD subjects—0
Explainable Data Poison Attacks on Human Emotion Evaluation Systems based on EEG SignalsCode0
Against Algorithmic Exploitation of Human Vulnerabilities—0
Explaining Imitation Learning through Frames—0
Hierarchical Explanations for Video Action RecognitionCode0
A Theoretical Framework for AI Models Explainability with Application in Biomedicine—0
GraphIX: Graph-based In silico XAI(explainable artificial intelligence) for drug repositioning from biopharmaceutical network—0
Trusting the Explainers: Teacher Validation of Explainable Artificial Intelligence for Course DesignCode0
Interpretable ML for Imbalanced DataCode0
Explainable Artificial Intelligence in Retinal Imaging for the detection of Systemic Diseases—0
Analysis of Explainable Artificial Intelligence Methods on Medical Image Classification—0
XRand: Differentially Private Defense against Explanation-Guided Attacks—0
MixBoost: Improving the Robustness of Deep Neural Networks by Boosting Data Augmentation—0
Going Beyond XAI: A Systematic Survey for Explanation-Guided Learning—0
Explainable Artificial Intelligence for Improved Modeling of ProcessesCode0
Foiling Explanations in Deep Neural NetworksCode0
Explainable Artificial Intelligence (XAI) from a user perspective- A synthesis of prior literature and problematizing avenues for future research—0
Crown-CAM: Interpretable Visual Explanations for Tree Crown Detection in Aerial Images—0
Revealing Hidden Context Bias in Segmentation and Object Detection through Concept-specific Explanations—0
Explainable Artificial Intelligence and Causal Inference based ATM Fraud Detection—0
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