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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 781790 of 971 papers

TitleStatusHype
A User-Centred Framework for Explainable Artificial Intelligence in Human-Robot Interaction0
Multihop: Leveraging Complex Models to Learn Accurate Simple Models0
When Stability meets Sufficiency: Informative Explanations that do not Overwhelm0
Adherence and Constancy in LIME-RS Explanations for Recommendation0
Longitudinal Distance: Towards Accountable Instance Attribution0
Knowledge-based XAI through CBR: There is more to explanations than models can tell0
Improvement of a Prediction Model for Heart Failure Survival through Explainable Artificial Intelligence0
Explainable Reinforcement Learning for Broad-XAI: A Conceptual Framework and Survey0
CARE: Coherent Actionable Recourse based on Sound Counterfactual ExplanationsCode0
Challenges for cognitive decoding using deep learning methods0
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