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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 551–600 of 971 papers

TitleStatusHype
Explainable AI in Grassland Monitoring: Enhancing Model Performance and Domain Adaptability—0
Explainable AI Integrated Feature Engineering for Wildfire Prediction—0
Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven decision support—0
Explainable AI meets Healthcare: A Study on Heart Disease Dataset—0
Explainable AI Methods for Multi-Omics Analysis: A Survey—0
Explainable AI needs formal notions of explanation correctness—0
Explainable AI over the Internet of Things (IoT): Overview, State-of-the-Art and Future Directions—0
Explainable-AI powered stock price prediction using time series transformers: A Case Study on BIST100—0
Explainable AI through the Learning of Arguments—0
Explainable AI via Learning to Optimize—0
Explainable Analysis of Deep Learning Methods for SAR Image Classification—0
Explainable Anomaly Detection: Counterfactual driven What-If Analysis—0
Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models—0
Explainable Artificial Intelligence Recommendation System by Leveraging the Semantics of Adverse Childhood Experiences: Proof-of-Concept Prototype Development—0
Explainable Artificial Intelligence Approaches: A Survey—0
Explainable Artificial Intelligence Architecture for Melanoma Diagnosis Using Indicator Localization and Self-Supervised Learning—0
Explainable Artificial Intelligence Techniques for Accurate Fault Detection and Diagnosis: A Review—0
Explainable Artificial Intelligence Techniques for Irregular Temporal Classification of Multidrug Resistance Acquisition in Intensive Care Unit Patients—0
Explainable Artificial Intelligence techniques for interpretation of food datasets: a review—0
Explainable Artificial Intelligence Techniques for Software Development Lifecycle: A Phase-specific Survey—0
Explainable Artificial Intelligence: a Systematic Review—0
Explainable Artificial Intelligence and Machine Learning: A reality rooted perspective—0
Explainable Artificial Intelligence and Causal Inference based ATM Fraud Detection—0
Explainable Artificial Intelligence and Cybersecurity: A Systematic Literature Review—0
Explainable Artificial Intelligence and its potential within Industry—0
Explainable Artificial Intelligence: A Survey of Needs, Techniques, Applications, and Future Direction—0
Explainable Artificial Intelligence Based Fault Diagnosis and Insight Harvesting for Steel Plates Manufacturing—0
Explainable Artificial Intelligence driven mask design for self-supervised seismic denoising—0
Explainable Artificial Intelligence for Process Mining: A General Overview and Application of a Novel Local Explanation Approach for Predictive Process Monitoring—0
Explainable Artificial Intelligence for Quantifying Interfering and High-Risk Behaviors in Autism Spectrum Disorder in a Real-World Classroom Environment Using Privacy-Preserving Video Analysis—0
Explainable Artificial Intelligence for Medical Applications: A Review—0
Explainable Artificial Intelligence for identifying profitability predictors in Financial Statements—0
Explainable artificial intelligence for mechanics: physics-informing neural networks for constitutive models—0
Explainable Artificial Intelligence for Autonomous Driving: A Comprehensive Overview and Field Guide for Future Research Directions—0
Explainable Artificial Intelligence for Pharmacovigilance: What Features Are Important When Predicting Adverse Outcomes?—0
Explainable Artificial Intelligence for Exhaust Gas Temperature of Turbofan Engines—0
Explainable Artificial Intelligence for Assault Sentence Prediction in New Zealand—0
Explainable artificial intelligence for Healthcare applications using Random Forest Classifier with LIME and SHAP—0
Explainable Artificial Intelligence for Drug Discovery and Development -- A Comprehensive Survey—0
Explainable Artificial Intelligence for Human Decision-Support System in Medical Domain—0
Explainable Artificial Intelligence for Smart City Application: A Secure and Trusted Platform—0
Shapley values for cluster importance: How clusters of the training data affect a prediction—0
Explainable Artificial Intelligence in Construction: The Content, Context, Process, Outcome Evaluation Framework—0
Explainable Artificial Intelligence in Retinal Imaging for the detection of Systemic Diseases—0
Explainable Artificial Intelligence in Biomedical Image Analysis: A Comprehensive Survey—0
Explainable artificial intelligence in breast cancer detection and risk prediction: A systematic scoping review—0
Explainable Artificial Intelligence Methods in Combating Pandemics: A Systematic Review—0
Explainable Artificial Intelligence Model for Evaluating Shear Strength Parameters of Municipal Solid Waste Across Diverse Compositional Profiles—0
Explainable artificial intelligence model to predict acute critical illness from electronic health records—0
eXplainable Artificial Intelligence on Medical Images: A Survey—0
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