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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 451–475 of 971 papers

TitleStatusHype
Beyond XAI:Obstacles Towards Responsible AI—0
Explaining Black-Box Algorithms Using Probabilistic Contrastive Counterfactuals—0
Achieving Diversity in Counterfactual Explanations: a Review and Discussion—0
Extending XReason: Formal Explanations for Adversarial Detection—0
Deep Learning for predicting rate-induced tipping—0
A general approach to compute the relevance of middle-level input features—0
Explaining Expert Search and Team Formation Systems with ExES—0
Deep Learning, Natural Language Processing, and Explainable Artificial Intelligence in the Biomedical Domain—0
Explaining Imitation Learning through Frames—0
Deep Learning Reproducibility and Explainable AI (XAI)—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
Enabling Machine Learning Algorithms for Credit Scoring -- Explainable Artificial Intelligence (XAI) methods for clear understanding complex predictive models—0
Explaining the Unexplainable: A Systematic Review of Explainable AI in Finance—0
Advancing Nearest Neighbor Explanation-by-Example with Critical Classification Regions—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
Dermatologist-like explainable AI enhances trust and confidence in diagnosing melanoma—0
Explain To Decide: A Human-Centric Review on the Role of Explainable Artificial Intelligence in AI-assisted Decision Making—0
Position: Explain to Question not to Justify—0
Explain yourself! Effects of Explanations in Human-Robot Interaction—0
Empowering Prior to Court Legal Analysis: A Transparent and Accessible Dataset for Defensive Statement Classification and Interpretation—0
Explanation-based Counterfactual Retraining(XCR): A Calibration Method for Black-box Models—0
Explanation-Guided Fair Federated Learning for Transparent 6G RAN Slicing—0
Beyond One-Size-Fits-All: Adapting Counterfactual Explanations to User Objectives—0
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