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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 251–275 of 971 papers

TitleStatusHype
ExplainableDetector: Exploring Transformer-based Language Modeling Approach for SMS Spam Detection with Explainability Analysis—0
LLMs for XAI: Future Directions for Explaining Explanations—0
Relevant Irrelevance: Generating Alterfactual Explanations for Image ClassifiersCode0
Explanation as a Watermark: Towards Harmless and Multi-bit Model Ownership Verification via Watermarking Feature AttributionCode1
Counterfactual and Semifactual Explanations in Abstract Argumentation: Formal Foundations, Complexity and Computation—0
False Sense of Security in Explainable Artificial Intelligence (XAI)—0
Isopignistic Canonical Decomposition via Belief Evolution Network—0
Explainable Interface for Human-Autonomy Teaming: A Survey—0
A Fresh Look at Sanity Checks for Saliency MapsCode1
Explainable Multi-Label Classification of MBTI Types—0
An Explainable and Conformal AI Model to Detect Temporomandibular Joint Involvement in Children Suffering from Juvenile Idiopathic Arthritis—0
Towards trustable SHAP scores—0
Mapping the Potential of Explainable AI for Fairness Along the AI Lifecycle—0
Fiper: a Visual-based Explanation Combining Rules and Feature Importance—0
T-Explainer: A Model-Agnostic Explainability Framework Based on Gradients—0
Explaining AI Decisions: Towards Achieving Human-Centered Explainability in Smart Home Environments—0
How should AI decisions be explained? Requirements for Explanations from the Perspective of European Law—0
Towards Robust Ferrous Scrap Material Classification with Deep Learning and Conformal Prediction—0
Concept Induction using LLMs: a user experiment for assessment—0
Explainable Lung Disease Classification from Chest X-Ray Images Utilizing Deep Learning and XAI—0
Explainable Artificial Intelligence Techniques for Accurate Fault Detection and Diagnosis: A Review—0
CNN-based explanation ensembling for dataset, representation and explanations evaluation—0
Reactive Model Correction: Mitigating Harm to Task-Relevant Features via Conditional Bias Suppression—0
Beyond One-Size-Fits-All: Adapting Counterfactual Explanations to User Objectives—0
Using Explainable AI and Transfer Learning to understand and predict the maintenance of Atlantic blocking with limited observational dataCode0
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