arXiv (All)
open access
A Synthetic Ground-Truth Framework for the Evaluation of Explainable AI Methods
Evaluating explainable Artificial Intelligence (XAI) methods is a challenging task due to the lack of reliable evaluation procedures and, in particular, the absence of ground truth explanations. In the literature, existing evaluation approaches typically assess explanations by measuring their fideli
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Modelling GDPR-Compliant Explanations for Trustworthy AI#409700
- A global taxonomy of interpretable AI: unifying the terminology for the technical and social sciences#107799
- Architecture-aware Robustness Evaluation of Explainable Deep Learning for Breast Cancer Diagnosis#1115648
- Visual Analytics for Explainable Deep Learning#478161
- Evaluating Explanation Methods by the Predictors They Induce#1033837
Record · ID 1104596
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive