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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

Showing 651675 of 971 papers

TitleStatusHype
The Influence of Explainable Artificial Intelligence: Nudging Behaviour or Boosting Capability?0
Higher-order Neural Additive Models: An Interpretable Machine Learning Model with Feature InteractionsCode0
OAK4XAI: Model towards Out-Of-Box eXplainable Artificial Intelligence for Digital Agriculture0
Greybox XAI: a Neural-Symbolic learning framework to produce interpretable predictions for image classificationCode0
Asset Pricing and Deep Learning0
Enhancing Cluster Analysis With Explainable AI and Multidimensional Cluster PrototypesCode0
Survey on Deep Fuzzy Systems in regression applications: a view on interpretability0
Responsibility: An Example-based Explainable AI approach via Training Process Inspection0
Regulating eXplainable Artificial Intelligence (XAI) May Harm Consumers0
Explainable Artificial Intelligence to Detect Image Spam Using Convolutional Neural Network0
Incremental Permutation Feature Importance (iPFI): Towards Online Explanations on Data Streams0
Generating detailed saliency maps using model-agnostic methods0
Identifying Dominant Industrial Sectors in Market States of the S&P 500 Financial Data0
Towards Benchmarking Explainable Artificial Intelligence Methods0
Explainable AI for tailored electricity consumption feedback -- an experimental evaluation of visualizations0
Augmented cross-selling through explainable AI -- a case from energy retailing0
SoK: Explainable Machine Learning for Computer Security ApplicationsCode0
Shapelet-Based Counterfactual Explanations for Multivariate Time SeriesCode0
Carefully choose the baseline: Lessons learned from applying XAI attribution methods for regression tasks in geoscience0
Causality-Inspired Taxonomy for Explainable Artificial Intelligence0
Explainable Reinforcement Learning on Financial Stock Trading using SHAP0
Transcending XAI Algorithm Boundaries through End-User-Inspired Design0
Explainable Artificial Intelligence for Assault Sentence Prediction in New Zealand0
An Empirical Comparison of Explainable Artificial Intelligence Methods for Clinical Data: A Case Study on Traumatic Brain InjuryCode0
Trustworthy Visual Analytics in Clinical Gait Analysis: A Case Study for Patients with Cerebral Palsy0
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