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 4150 of 971 papers

TitleStatusHype
Beyond Pixels: Enhancing LIME with Hierarchical Features and Segmentation Foundation ModelsCode1
Embedded Encoder-Decoder in Convolutional Networks Towards Explainable AICode1
A Song of (Dis)agreement: Evaluating the Evaluation of Explainable Artificial Intelligence in Natural Language ProcessingCode1
Explainable AI for Bioinformatics: Methods, Tools, and ApplicationsCode1
Explainable Deep Learning Methods in Medical Image Classification: A SurveyCode1
Explainable Earth Surface Forecasting under Extreme EventsCode1
Extending CAM-based XAI methods for Remote Sensing Imagery SegmentationCode1
Extracting human interpretable structure-property relationships in chemistry using XAI and large language modelsCode1
ExpPoint-MAE: Better interpretability and performance for self-supervised point cloud transformersCode1
BASED-XAI: Breaking Ablation Studies Down for Explainable Artificial IntelligenceCode1
Show:102550
← PrevPage 5 of 98Next →

No leaderboard results yet.