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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 51–75 of 971 papers

TitleStatusHype
Explainable AI-Guided Efficient Approximate DNN Generation for Multi-Pod Systolic Arrays—0
Logic Explanation of AI Classifiers by Categorical Explaining Functors—0
Explainable AI Components for Narrative Map ExtractionCode1
Automated Processing of eXplainable Artificial Intelligence Outputs in Deep Learning Models for Fault Diagnostics of Large Infrastructures—0
Guarding Digital Privacy: Exploring User Profiling and Security Enhancements—0
Securing Virtual Reality Experiences: Unveiling and Tackling Cybersickness Attacks with Explainable AI—0
Computational identification of ketone metabolism as a key regulator of sleep stability and circadian dynamics via real-time metabolic profiling—0
A Data-Driven Exploration of Elevation Cues in HRTFs: An Explainable AI Perspective Across Multiple Datasets—0
GENEOnet: Statistical analysis supporting explainability and trustworthiness—0
Explaining the Unexplainable: A Systematic Review of Explainable AI in Finance—0
Robust Intrusion Detection System with Explainable Artificial Intelligence—0
ILLC: Iterative Layer-by-Layer Compression for Enhancing Structural Faithfulness in SpArX—0
Exploring specialization and sensitivity of convolutional neural networks in the context of simultaneous image augmentationsCode0
The Role of Deep Learning in Financial Asset Management: A Systematic Review—0
GNN-XAR: A Graph Neural Network for Explainable Activity Recognition in Smart Homes—0
Class-Dependent Perturbation Effects in Evaluating Time Series AttributionsCode0
Doctor-in-the-Loop: An Explainable, Multi-View Deep Learning Framework for Predicting Pathological Response in Non-Small Cell Lung Cancer—0
Explainable Artificial Intelligence Model for Evaluating Shear Strength Parameters of Municipal Solid Waste Across Diverse Compositional Profiles—0
Explainable AI-Driven Neural Activity Analysis in Parkinsonian Rats under Electrical Stimulation—0
ExplainReduce: Summarising local explanations via proxiesCode0
PixleepFlow: A Pixel-Based Lifelog Framework for Predicting Sleep Quality and Stress LevelCode0
This looks like what? Challenges and Future Research Directions for Part-Prototype ModelsCode0
Finding Words Associated with DIF: Predicting Differential Item Functioning using LLMs and Explainable AI—0
Using agent-based models and EXplainable Artificial Intelligence (XAI) to simulate social behaviors and policy intervention scenarios: A case study of private well users in Ireland—0
Coherent Local Explanations for Mathematical OptimizationCode0
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