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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 551–600 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
The Literature Review Network: An Explainable Artificial Intelligence for Systematic Literature Reviews, Meta-analyses, and Method Development—0
Theoretical Behavior of XAI Methods in the Presence of Suppressor Variables—0
The Pragmatic Turn in Explainable Artificial Intelligence (XAI)—0
The role of causality in explainable artificial intelligence—0
The Role of Deep Learning in Financial Asset Management: A Systematic Review—0
The Role of XAI in Transforming Aeronautics and Aerospace Systems—0
The Thousand Faces of Explainable AI Along the Machine Learning Life Cycle: Industrial Reality and Current State of Research—0
TimeREISE: Time-series Randomized Evolving Input Sample Explanation—0
Toward Affective XAI: Facial Affect Analysis for Understanding Explainable Human-AI Interactions—0
Towards a general framework for improving the performance of classifiers using XAI methods—0
Towards a Grounded Dialog Model for Explainable Artificial Intelligence—0
Towards an Evaluation Framework for Explainable Artificial Intelligence Systems for Health and Well-being—0
Towards a Rigorous Evaluation of Explainability for Multivariate Time Series—0
Towards a Rigorous Evaluation of XAI Methods on Time Series—0
Towards a Shapley Value Graph Framework for Medical peer-influence—0
Towards Benchmarking Explainable Artificial Intelligence Methods—0
When Stability meets Sufficiency: Informative Explanations that do not Overwhelm—0
Towards explainable meta-learning—0
Towards Explainable Artificial Intelligence—0
Towards Explainable Artificial Intelligence (XAI): A Data Mining Perspective—0
Towards Harnessing Natural Language Generation to Explain Black-box Models—0
Towards Human Cognition Level-based Experiment Design for Counterfactual Explanations (XAI)—0
Towards Interpretability in Audio and Visual Affective Machine Learning: A Review—0
Towards Interpretable and Efficient Feature Selection in Trajectory Datasets: A Taxonomic Approach—0
Towards Quantification of Explainability in Explainable Artificial Intelligence Methods—0
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