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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 831840 of 971 papers

TitleStatusHype
Explainable Artificial Intelligence for Dependent Features: Additive Effects of CollinearityCode0
Impact of lung segmentation on the diagnosis and explanation of COVID-19 in chest X-ray imagesCode0
Impact of satellites streaks for observational astronomy: a study on data captured during one year from Luxembourg Greater RegionCode0
People Attribute Purpose to Autonomous Vehicles When Explaining Their Behavior: Insights from Cognitive Science for Explainable AICode0
PIC-XAI: Post-hoc Image Captioning Explanation using SegmentationCode0
PixleepFlow: A Pixel-Based Lifelog Framework for Predicting Sleep Quality and Stress LevelCode0
Challenges facing the explainability of age prediction models: case study for two modalitiesCode0
Explainable Artificial Intelligence for Bayesian Neural Networks: Towards trustworthy predictions of ocean dynamicsCode0
Towards Explainable Multimodal Depression Recognition for Clinical InterviewsCode0
Explainable Artificial Intelligence for Improved Modeling of ProcessesCode0
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