mlsanityData to 7 Oct 2026

Causality-Inspired Taxonomy for Explainable Artificial Intelligence

arXiv 2208.09500 ↗First posted 19 Aug 2022cs.CVcited by 9

Not on the map. Why?

Abstract (arXiv)

As two sides of the same coin, causality and explainable artificial intelligence (xAI) were initially proposed and developed with different goals. However, the latter can only be complete when seen through the lens of the causality framework. As such, we propose a novel causality-inspired framework for xAI that creates an environment for the development of xAI approaches. To show its applicability, biometrics was used as case study. For this, we have analysed 81 research papers on a myriad of biometric modalities and different tasks. We have categorised each of these methods according to our novel xAI Ladder and discussed the future directions of the field.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 84 of its 226 references that are in the map. First posted on arXiv 19 Aug 2022.

Builds on

84 of 226 references.

References · 84

  1. 2017Attention Is All You Needcited by 59,932
  2. 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,690
  3. 2017Axiomatic Attribution for Deep Networkscited by 3,350
  4. 2015VQA: Visual Question Answeringcited by 2,489
  5. 2015Learning Deep Features for Discriminative Localizationcited by 2,430
  6. 2013Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Mapscited by 2,348
  7. 2013Visualizing and Understanding Convolutional Networkscited by 2,278
  8. 2019Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AIcited by 1,543
  9. 2017Learning Important Features Through Propagating Activation Differencescited by 1,454
  10. 2017Towards A Rigorous Science of Interpretable Machine Learningcited by 1,400
  11. 2017SmoothGrad: removing noise by adding noisecited by 1,097
  12. 2014Striving for Simplicity: The All Convolutional Netcited by 983
38 more
  1. 2018A Survey Of Methods For Explaining Black Box Modelscited by 970
  2. 2018RISE: Randomized Input Sampling for Explanation of Black-box Modelscited by 825
  3. 2018This Looks Like That: Deep Learning for Interpretable Image Recognitioncited by 731
  4. 2017Network Dissection: Quantifying Interpretability of Deep Visual Representationscited by 703
  5. 2019Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networkscited by 575
  6. 2018Explaining Explanations: An Overview of Interpretability of Machine Learningcited by 508
  7. 2013Do Deep Nets Really Need to be Deep?cited by 466
  8. 2015Evaluating the visualization of what a Deep Neural Network has learnedcited by 416
  9. 2017Methods for Interpreting and Understanding Deep Neural Networkscited by 342
  10. 2019Fooling automated surveillance cameras: adversarial patches to attack person detectioncited by 268
  11. 2020Opportunities and Challenges in Explainable Artificial Intelligence (XAI): A Surveycited by 252
  12. 2020Concept Whitening for Interpretable Image Recognitioncited by 225
  13. 2020Neural Prototype Trees for Interpretable Fine-grained Image Recognitioncited by 213
  14. 2017Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Modelscited by 202
  15. 2019Causality matters in medical imagingcited by 193
  16. 2018Multimodal Explanations: Justifying Decisions and Pointing to the Evidencecited by 179
  17. 2019Efficient Decision-based Black-box Adversarial Attacks on Face Recognitioncited by 173
  18. 2020Searching Central Difference Convolutional Networks for Face Anti-Spoofingcited by 173
  19. 2015Inverting Visual Representations with Convolutional Networkscited by 168
  20. 2018Learning Deep Models for Face Anti-Spoofing: Binary or Auxiliary Supervisioncited by 167
  21. 2016Generating Visual Explanationscited by 156
  22. 2020FastIF: Scalable Influence Functions for Efficient Model Interpretation and Debuggingcited by 120
  23. 2021Deformable ProtoPNet: An Interpretable Image Classifier Using Deformable Prototypescited by 120
  24. 2019"How do I fool you?": Manipulating User Trust via Misleading Black Box Explanationscited by 111
  25. 2020Demographic Bias in Biometrics: A Survey on an Emerging Challengecited by 93
  26. 2019Regularized Fine-grained Meta Face Anti-spoofingcited by 86
  27. 2021A Comprehensive Study on Face Recognition Biases Beyond Demographicscited by 86
  28. 2020Towards Transferable Adversarial Attack against Deep Face Recognitioncited by 79
  29. 2019Interpretable and Steerable Sequence Learning via Prototypescited by 72
  30. 2020Deep Spatial Gradient and Temporal Depth Learning for Face Anti-spoofingcited by 70
  31. 2021CR-FIQA: Face Image Quality Assessment by Learning Sample Relative Classifiabilitycited by 68
  32. 2019Interpretable Image Recognition with Hierarchical Prototypescited by 64
  33. 2020On Disentangling Spoof Trace for Generic Face Anti-Spoofingcited by 62
  34. 2018Face De-Spoofing: Anti-Spoofing via Noise Modelingcited by 60
  35. 2022Meta Balanced Network for Fair Face Recognitioncited by 53
  36. 2019Characterizing the Variability in Face Recognition Accuracy Relative to Racecited by 40
  37. 2018Improving the Interpretability of Deep Neural Networks with Knowledge Distillationcited by 37
  38. 2021Group-CAM: Group Score-Weighted Visual Explanations for Deep Convolutional Networkscited by 36

50 most cited of 84 All 84 (JSON)

Built on by

Cited by year

Citing papers per year: 2022: 1; 2023: 4; 2024: 3; 2025: 1.

9 papers.

  1. 2023Unveiling the Two-Faced Truth: Disentangling Morphed Identities for Face Morphing Detectioncited by 9
  2. 2023Compressed Models Decompress Race Biases: What Quantized Models Forget for Fair Face Recognitioncited by 9
  3. 2023Are Explainability Tools Gender Biased? A Case Study on Face Presentation Attack Detectioncited by 6
  4. 2022PIC-Score: Probabilistic Interpretable Comparison Score for Optimal Matching Confidence in Single- and Multi-Biometric (Face) Recognitioncited by 4
  5. 2023Discriminative Deep Feature Visualization for Explainable Face Recognitioncited by 3
  6. 2024Fairness Under Cover: Evaluating the Impact of Occlusions on Demographic Bias in Facial Recognitioncited by 3
  7. 2024MST-KD: Multiple Specialized Teachers Knowledge Distillation for Fair Face Recognitioncited by 2
  8. 2024Spoof Trace Discovery for Deep Learning Based Explainable Face Anti-Spoofingcited by 0
  9. 2025Balancing Beyond Discrete Categories: Continuous Demographic Labels for Fair Face Recognitioncited by 0