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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 126–150 of 971 papers

TitleStatusHype
Automatic explanation of the classification of Spanish legal judgments in jurisdiction-dependent law categories with tree estimators—0
A Survey on Explainable Artificial Intelligence (XAI): Towards Medical XAI—0
A survey on Concept-based Approaches For Model Improvement—0
Analysis and Evaluation of Explainable Artificial Intelligence on Suicide Risk Assessment—0
A Complete Characterisation of ReLU-Invariant Distributions—0
Classifying Simulated Gait Impairments using Privacy-preserving Explainable Artificial Intelligence and Mobile Phone Videos—0
A Survey of Explainable Knowledge Tracing—0
A Survey of Explainable Artificial Intelligence (XAI) in Financial Time Series Forecasting—0
A Multi-Modal Explainability Approach for Human-Aware Robots in Multi-Party Conversation—0
A Survey of Explainable AI and Proposal for a Discipline of Explanation Engineering—0
A Survey of Accessible Explainable Artificial Intelligence Research—0
A Brief Review of Explainable Artificial Intelligence in Healthcare—0
Asset Pricing and Deep Learning—0
Assessing high-order effects in feature importance via predictability decomposition—0
A multi-component framework for the analysis and design of explainable artificial intelligence—0
Am I Building a White Box Agent or Interpreting a Black Box Agent?—0
A Comparative Approach to Explainable Artificial Intelligence Methods in Application to High-Dimensional Electronic Health Records: Examining the Usability of XAI—0
ChatGPT or Human? Detect and Explain. Explaining Decisions of Machine Learning Model for Detecting Short ChatGPT-generated Text—0
Clash of the Explainers: Argumentation for Context-Appropriate Explanations—0
CNN-based explanation ensembling for dataset, representation and explanations evaluation—0
Comparative Analysis of Hyperspectral Image Reconstruction Using Deep Learning for Agricultural and Biological Applications—0
A Series of Unfortunate Counterfactual Events: the Role of Time in Counterfactual Explanations—0
aSAGA: Automatic Sleep Analysis with Gray Areas—0
Am I Being Treated Fairly? A Conceptual Framework for Individuals to Ascertain Fairness—0
ARTxAI: Explainable Artificial Intelligence Curates Deep Representation Learning for Artistic Images using Fuzzy Techniques—0
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