SOTAVerified

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 681690 of 971 papers

TitleStatusHype
From Data to Commonsense Reasoning: The Use of Large Language Models for Explainable AI0
From Interpretable Filters to Predictions of Convolutional Neural Networks with Explainable Artificial Intelligence0
From Motion to Meaning: Biomechanics-Informed Neural Network for Explainable Cardiovascular Disease Identification0
From Pixels to Words: Leveraging Explainability in Face Recognition through Interactive Natural Language Processing0
From Robustness to Explainability and Back Again0
From SHAP Scores to Feature Importance Scores0
From What Ifs to Insights: Counterfactuals in Causal Inference vs. Explainable AI0
GENEOnet: Statistical analysis supporting explainability and trustworthiness0
Generating detailed saliency maps using model-agnostic methods0
Geometric Remove-and-Retrain (GOAR): Coordinate-Invariant eXplainable AI Assessment0
Show:102550
← PrevPage 69 of 98Next →

No leaderboard results yet.