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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 351–375 of 971 papers

TitleStatusHype
An Artificial Intelligence-based model for cell killing prediction: development, validation and explainability analysis of the ANAKIN model—0
Adherence and Constancy in LIME-RS Explanations for Recommendation—0
Abstraction, Validation, and Generalization for Explainable Artificial Intelligence—0
Deciphering AutoML Ensembles: cattleia's Assistance in Decision-Making—0
DCNFIS: Deep Convolutional Neuro-Fuzzy Inference System—0
A Theoretical Framework for AI Models Explainability with Application in Biomedicine—0
Dataset | Mindset = Explainable AI | Interpretable AI—0
Data Representing Ground-Truth Explanations to Evaluate XAI Methods—0
A Temporal Type-2 Fuzzy System for Time-dependent Explainable Artificial Intelligence—0
An Argumentation-based Approach for Explaining Goal Selection in Intelligent Agents—0
Data integration in systems genetics and aging research—0
DA-DGCEx: Ensuring Validity of Deep Guided Counterfactual Explanations With Distribution-Aware Autoencoder Loss—0
Crown-CAM: Interpretable Visual Explanations for Tree Crown Detection in Aerial Images—0
A Systematic Review of User-Centred Evaluation of Explainable AI in Healthcare—0
A Deep Generative XAI Framework for Natural Language Inference Explanations Generation—0
Creating an Explainable Intrusion Detection System Using Self Organizing Maps—0
A Survey on Understanding, Visualizations, and Explanation of Deep Neural Networks—0
Counterfactuals and Causability in Explainable Artificial Intelligence: Theory, Algorithms, and Applications—0
A Survey on Explainable Artificial Intelligence for Cybersecurity—0
Analysis of Explainable Artificial Intelligence Methods on Medical Image Classification—0
Counterfactual Formulation of Patient-Specific Root Causes of Disease—0
Counterfactual Explanations of Black-box Machine Learning Models using Causal Discovery with Applications to Credit Rating—0
A Survey on Explainable Artificial Intelligence (XAI): Towards Medical XAI—0
Counterfactual Explanations for Clustering Models—0
A survey on Concept-based Approaches For Model Improvement—0
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