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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 626–650 of 971 papers

TitleStatusHype
Explainable Lung Disease Classification from Chest X-Ray Images Utilizing Deep Learning and XAI—0
Explainable Machine Learning-Based Security and Privacy Protection Framework for Internet of Medical Things Systems—0
Explainable Machine Learning for Predicting Homicide Clearance in the United States—0
Explainable Artificial Intelligence (XAI) for Malware Analysis: A Survey of Techniques, Applications, and Open Challenges—0
Explainable Multi-Label Classification of MBTI Types—0
Explainable Multimodal Sentiment Analysis on Bengali Memes—0
Explainable Predictive Maintenance—0
Explainable Reinforcement Learning: A Survey—0
Explainable Reinforcement Learning for Broad-XAI: A Conceptual Framework and Survey—0
Explainable Reinforcement Learning on Financial Stock Trading using SHAP—0
Explainable Security—0
Explaining a Deep Reinforcement Learning Docking Agent Using Linear Model Trees with User Adapted Visualization—0
Explaining AI Decisions: Towards Achieving Human-Centered Explainability in Smart Home Environments—0
Explaining Any ML Model? -- On Goals and Capabilities of XAI—0
Explaining automated gender classification of human gait—0
Explaining Black-Box Algorithms Using Probabilistic Contrastive Counterfactuals—0
Explaining Expert Search and Team Formation Systems with ExES—0
Explaining Imitation Learning through Frames—0
Explaining machine learning models for age classification in human gait analysis—0
Explaining the Deep Natural Language Processing by Mining Textual Interpretable Features—0
Explaining the Impact of Training on Vision Models via Activation Clustering—0
Explaining the Unexplainable: A Systematic Review of Explainable AI in Finance—0
Do Explanations Reflect Decisions? A Machine-centric Strategy to Quantify the Performance of Explainability Algorithms—0
EXPLAIN-IT: Towards Explainable AI for Unsupervised Network Traffic Analysis—0
Explain To Decide: A Human-Centric Review on the Role of Explainable Artificial Intelligence in AI-assisted Decision Making—0
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