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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 551–575 of 971 papers

TitleStatusHype
SketchXAI: A First Look at Explainability for Human Sketches—0
SolderNet: Towards Trustworthy Visual Inspection of Solder Joints in Electronics Manufacturing Using Explainable Artificial Intelligence—0
Some Insights Towards a Unified Semantic Representation of Explanation for eXplainable Artificial Intelligence—0
Squashing activation functions in benchmark tests: towards eXplainable Artificial Intelligence using continuous-valued logic—0
Stacked ensemble\-based mutagenicity prediction model using multiple modalities with graph attention network—0
STARdom: an architecture for trusted and secure human-centered manufacturing systems—0
Statistical tuning of artificial neural network—0
Stop ordering machine learning algorithms by their explainability! A user-centered investigation of performance and explainability—0
Strategies to exploit XAI to improve classification systems—0
Survey of XAI in digital pathology—0
Survey on Deep Fuzzy Systems in regression applications: a view on interpretability—0
Sustainable Personalisation and Explainability in Dyadic Data Systems—0
Symbolic Knowledge Extraction and Injection with Sub-symbolic Predictors: A Systematic Literature Review—0
TbExplain: A Text-based Explanation Method for Scene Classification Models with the Statistical Prediction Correction—0
Tell me more: Intent Fulfilment Framework for Enhancing User Experiences in Conversational XAI—0
T-Explainer: A Model-Agnostic Explainability Framework Based on Gradients—0
The Anatomy of Adversarial Attacks: Concept-based XAI Dissection—0
The Challenge of Imputation in Explainable Artificial Intelligence Models—0
The Conflict Between Explainable and Accountable Decision-Making Algorithms—0
The Dark Side of Explanations: Poisoning Recommender Systems with Counterfactual Examples—0
The design and implementation of Language Learning Chatbot with XAI using Ontology and Transfer Learning—0
The Disagreement Problem in Faithfulness Metrics—0
The future of human-centric eXplainable Artificial Intelligence (XAI) is not post-hoc explanations—0
The Influence of Explainable Artificial Intelligence: Nudging Behaviour or Boosting Capability?—0
The Limits of Perception: Analyzing Inconsistencies in Saliency Maps in XAI—0
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