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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 901–925 of 971 papers

TitleStatusHype
Explainable AI-based Intrusion Detection System for Industry 5.0: An Overview of the Literature, associated Challenges, the existing Solutions, and Potential Research Directions—0
Explainable AI: current status and future directions—0
Explainable AI does not provide the explanations end-users are asking for—0
Explainable AI-Driven Neural Activity Analysis in Parkinsonian Rats under Electrical Stimulation—0
Explainable AI for Earth Observation: Current Methods, Open Challenges, and Opportunities—0
Explainable AI for Embedded Systems Design: A Case Study of Static Redundant NVM Memory Write Prediction—0
Explainable AI for tailored electricity consumption feedback -- an experimental evaluation of visualizations—0
Explainable AI for Time Series via Virtual Inspection Layers—0
Explainable AI for tool wear prediction in turning—0
Explainable AI-Guided Efficient Approximate DNN Generation for Multi-Pod Systolic Arrays—0
Explainable AI in Diagnosing and Anticipating Leukemia Using Transfer Learning Method—0
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
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