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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 76–100 of 971 papers

TitleStatusHype
A Wearable Device Dataset for Mental Health Assessment Using Laser Doppler Flowmetry and Fluorescence Spectroscopy SensorsCode1
Uncertainty Quantification of Wind Gust Predictions in the Northeast US: An Evidential Neural Network and Explainable Artificial Intelligence Approach—0
Concept-Based Explainable Artificial Intelligence: Metrics and Benchmarks—0
Towards Transparent and Accurate Diabetes Prediction Using Machine Learning and Explainable Artificial Intelligence—0
Explainable Artificial Intelligence for identifying profitability predictors in Financial Statements—0
Is Conversational XAI All You Need? Human-AI Decision Making With a Conversational XAI AssistantCode0
Exact Computation of Any-Order Shapley Interactions for Graph Neural Networks—0
Towards Explainable Multimodal Depression Recognition for Clinical InterviewsCode0
Symbolic Knowledge Extraction and Injection with Sub-symbolic Predictors: A Systematic Literature Review—0
Ensuring Medical AI Safety: Explainable AI-Driven Detection and Mitigation of Spurious Model Behavior and Associated DataCode0
EVolutionary Independent DEtermiNistiC Explanation—0
DLinear-based Prediction of Remaining Useful Life of Lithium-Ion Batteries: Feature Engineering through Explainable Artificial Intelligence—0
Conditional Feature Importance with Generative Modeling Using Adversarial Random ForestsCode0
Enhancing AI Transparency: XRL-Based Resource Management and RAN Slicing for 6G ORAN Architecture—0
Explainable artificial intelligence (XAI): from inherent explainability to large language models—0
Polynomial Threshold Functions of Bounded Tree-Width: Some Explainability and Complexity Aspects—0
Explainable Federated Bayesian Causal Inference and Its Application in Advanced ManufacturingCode0
Integrating Explainable AI for Effective Malware Detection in Encrypted Network Traffic—0
Found in Translation: semantic approaches for enhancing AI interpretability in face verification—0
Leveraging Explainable AI for LLM Text Attribution: Differentiating Human-Written and Multiple LLMs-Generated Text—0
Detecting AI-Generated Text in Educational Content: Leveraging Machine Learning and Explainable AI for Academic Integrity—0
FitCF: A Framework for Automatic Feature Importance-guided Counterfactual Example GenerationCode0
Extending XReason: Formal Explanations for Adversarial Detection—0
The Role of XAI in Transforming Aeronautics and Aerospace Systems—0
Enhancing Cancer Diagnosis with Explainable & Trustworthy Deep Learning Models—0
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