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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 526–550 of 971 papers

TitleStatusHype
Representation, Justification and Explanation in a Value Driven Agent: An Argumentation-Based Approach—0
Reputation-Based Federated Learning Defense to Mitigate Threats in EEG Signal Classification—0
Research on Older Adults' Interaction with E-Health Interface Based on Explainable Artificial Intelligence—0
Resisting Out-of-Distribution Data Problem in Perturbation of XAI—0
Responsibility: An Example-based Explainable AI approach via Training Process Inspection—0
Why is plausibility surprisingly problematic as an XAI criterion?—0
Revealing Hidden Context Bias in Segmentation and Object Detection through Concept-specific Explanations—0
Revealing Vulnerabilities of Neural Networks in Parameter Learning and Defense Against Explanation-Aware Backdoors—0
Revisiting the Performance-Explainability Trade-Off in Explainable Artificial Intelligence (XAI)—0
Revisiting the robustness of post-hoc interpretability methods—0
Robust and Explainable Framework to Address Data Scarcity in Diagnostic Imaging—0
Robust Intrusion Detection System with Explainable Artificial Intelligence—0
Robustness of Explainable Artificial Intelligence in Industrial Process Modelling—0
Rough Randomness and its Application—0
Safety design concepts for statistical machine learning components toward accordance with functional safety standards—0
Safety Implications of Explainable Artificial Intelligence in End-to-End Autonomous Driving—0
Sampling - Variational Auto Encoder - Ensemble: In the Quest of Explainable Artificial Intelligence—0
Scalable Concept Extraction in Industry 4.0—0
SCENE: Evaluating Explainable AI Techniques Using Soft Counterfactuals—0
Securing Virtual Reality Experiences: Unveiling and Tackling Cybersickness Attacks with Explainable AI—0
Self-eXplainable AI for Medical Image Analysis: A Survey and New Outlooks—0
Self-Supervised Interpretable End-to-End Learning via Latent Functional Modularity—0
Regulating eXplainable Artificial Intelligence (XAI) May Harm Consumers—0
X-SHIELD: Regularization for eXplainable Artificial Intelligence—0
Should We Trust (X)AI? Design Dimensions for Structured Experimental Evaluations—0
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