mlsanityData to 14 Oct 2026

A novel approach to generate datasets with XAI ground truth to evaluate image models

arXiv 2302.05624 ↗First posted 11 Feb 2023cs.CV cs.AI cs.LGcited by 4

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Abstract (arXiv)

With the increased usage of artificial intelligence (AI), it is imperative to understand how these models work internally. These needs have led to the development of a new field called eXplainable artificial intelligence (XAI). This field consists of on a set of techniques that allows us to theoretically determine the cause of the AI decisions. One main issue of XAI is how to verify the works on this field, taking into consideration the lack of ground truth (GT). In this study, we propose a new method to generate datasets with GT. We conducted a set of experiments that compared our GT with real model explanations and obtained excellent results confirming that our proposed method is correct.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 17 of its 26 references that are in the map. First posted on arXiv 11 Feb 2023.

Builds on

17 of 26 references.

References · 17

  1. 2017Attention Is All You Needcited by 60,361
  2. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,070
  3. 2019Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AIcited by 1,552
  4. 2017Explanation in Artificial Intelligence: Insights from the Social Sciencescited by 1,206
  5. 2018Sanity Checks for Saliency Mapscited by 1,156
  6. 2018RISE: Randomized Input Sampling for Explanation of Black-box Modelscited by 838
  7. 2018Towards Robust Interpretability with Self-Explaining Neural Networkscited by 505
  8. 2019Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methodscited by 443
  9. 2015Evaluating the visualization of what a Deep Neural Network has learnedcited by 419
  10. 2018On the Robustness of Interpretability Methodscited by 314
  11. 2019Interpretable machine learning: definitions, methods, and applicationscited by 305
  12. 2021Explainable artificial intelligence (XAI) in deep learning-based medical image analysiscited by 147
5 more
  1. 2020Ground Truth Evaluation of Neural Network Explanations with CLEVR-XAIcited by 126
  2. 2019Sanity Checks for Saliency Metricscited by 119
  3. 2022Metrics for saliency map evaluation of deep learning explanation methodscited by 40
  4. 2020OptiLIME: Optimized LIME Explanations for Diagnostic Computer Algorithmscited by 30
  5. 2022Investigating the fidelity of explainable artificial intelligence methods for applications of convolutional neural networks in geosciencecited by 28

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Cited by year

Citing papers per year: 2023: 2; 2024: 2.

4 papers.

  1. 2024A comprehensive study on fidelity metrics for XAIcited by 16
  2. 2023Assessing Fidelity in XAI post-hoc techniques: A Comparative Study with Ground Truth Explanations Datasetscited by 15
  3. 2023How Well Do Feature-Additive Explainers Explain Feature-Additive Predictors?cited by 2
  4. 2024Meta-evaluating stability measures: MAX-Senstivity & AVG-Sensitivitycited by 1