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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

Showing 451475 of 971 papers

TitleStatusHype
Explainable artificial intelligence for Healthcare applications using Random Forest Classifier with LIME and SHAP0
Explaining Black-Box Algorithms Using Probabilistic Contrastive Counterfactuals0
Concept-based Explainable Artificial Intelligence: A Survey0
Explainable Artificial Intelligence for Assault Sentence Prediction in New Zealand0
Explainable Artificial Intelligence for Exhaust Gas Temperature of Turbofan Engines0
Explainable Artificial Intelligence for Pharmacovigilance: What Features Are Important When Predicting Adverse Outcomes?0
Explaining Expert Search and Team Formation Systems with ExES0
Concept-Attention Whitening for Interpretable Skin Lesion Diagnosis0
Explaining Imitation Learning through Frames0
Am I Building a White Box Agent or Interpreting a Black Box Agent?0
Explaining machine learning models for age classification in human gait analysis0
Explaining the Deep Natural Language Processing by Mining Textual Interpretable Features0
A Comparative Approach to Explainable Artificial Intelligence Methods in Application to High-Dimensional Electronic Health Records: Examining the Usability of XAI0
A Brief Review of Explainable Artificial Intelligence in Healthcare0
Recent Advances in Medical Image Classification0
Explainable Artificial Intelligence for Autonomous Driving: A Comprehensive Overview and Field Guide for Future Research Directions0
Explainable artificial intelligence for mechanics: physics-informing neural networks for constitutive models0
Computational identification of ketone metabolism as a key regulator of sleep stability and circadian dynamics via real-time metabolic profiling0
Explain To Decide: A Human-Centric Review on the Role of Explainable Artificial Intelligence in AI-assisted Decision Making0
Position: Explain to Question not to Justify0
Explain yourself! Effects of Explanations in Human-Robot Interaction0
Explainable Artificial Intelligence for identifying profitability predictors in Financial Statements0
Explanation-based Counterfactual Retraining(XCR): A Calibration Method for Black-box Models0
Explanation-Guided Fair Federated Learning for Transparent 6G RAN Slicing0
Explainable Artificial Intelligence for Medical Applications: A Review0
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