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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 551–575 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
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