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Towards Robust Interpretability with Self-Explaining Neural Networks

arXiv 1806.07538 ↗First posted 20 Jun 2018cs.LG stat.MLcited by 505

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Abstract (arXiv)

Most recent work on interpretability of complex machine learning models has focused on estimating textita posteriori explanations for previously trained models around specific predictions. textitSelf-explaining models where interpretability plays a key role already during learning have received much less attention. We propose three desiderata for explanations in general -- explicitness, faithfulness, and stability -- and show that existing methods do not satisfy them. In response, we design self-explaining models in stages, progressively generalizing linear classifiers to complex yet architecturally explicit models. Faithfulness and stability are enforced via regularization specifically tailored to such models. Experimental results across various benchmark datasets show that our framework offers a promising direction for reconciling model complexity and interpretability.

Key facts. Cited by 505 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Jun 2018.

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Citing papers per year: 2021: 91; 2022: 93; 2023: 90; 2024: 102; 2025: 78; 2026: 51.

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  1. 2021Deep Neural Networks and Tabular Data: A Surveycited by 489
  2. 2022From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AIcited by 299
  3. 2022The Alignment Problem from a Deep Learning Perspectivecited by 246
  4. 2021Trustworthy AI: From Principles to Practicescited by 206
  5. 2022Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations and Beyondcited by 171
  6. 2021Benchmarking and Survey of Explanation Methods for Black Box Modelscited by 136
  7. 2022Towards Faithful Model Explanation in NLP: A Surveycited by 129
  8. 2021Teach Me to Explain: A Review of Datasets for Explainable Natural Language Processingcited by 126
  9. 2022Toward Transparent AI: A Survey on Interpreting the Inner Structures of Deep Neural Networkscited by 126
  10. 2021Human-Centered Explainable AI (XAI): From Algorithms to User Experiencescited by 123
  11. 2021Acquisition of Chess Knowledge in AlphaZerocited by 120
  12. 2023Probabilistic Concept Bottleneck Modelscited by 101
38 more
  1. 2021Interpretable Image Classification with Differentiable Prototypes Assignmentcited by 97
  2. 2023Concept-based Explainable Artificial Intelligence: A Surveycited by 97
  3. 2021CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithmscited by 85
  4. 2022Towards Human-centered Explainable AI: A Survey of User Studies for Model Explanationscited by 84
  5. 2021Few-Shot Self-Rationalization with Natural Language Promptscited by 83
  6. 2021Explainability of deep vision-based autonomous driving systems: Review and challengescited by 80
  7. 2022Concept Bottleneck Model with Additional Unsupervised Conceptscited by 79
  8. 2022Diffusion Models for Counterfactual Explanationscited by 79
  9. 2021Towards Self-Explainable Graph Neural Networkcited by 75
  10. 2022GlanceNets: Interpretabile, Leak-proof Concept-based Modelscited by 73
  11. 2023Evaluating Large Language Models on Controlled Generation Taskscited by 73
  12. 2021XProtoNet: Diagnosis in Chest Radiography with Global and Local Explanationscited by 72
  13. 2023Discover and Cure: Concept-aware Mitigation of Spurious Correlationcited by 71
  14. 2024VLG-CBM: Training Concept Bottleneck Models with Vision-Language Guidancecited by 65
  15. 2023A Closer Look at the Intervention Procedure of Concept Bottleneck Modelscited by 63
  16. 2022Concept Embedding Models: Beyond the Accuracy-Explainability Trade-Offcited by 61
  17. 2021Synthetic Benchmarks for Scientific Research in Explainable Machine Learningcited by 59
  18. 2022Visual Recognition with Deep Nearest Centroidscited by 59
  19. 2021How can I choose an explainer? An Application-grounded Evaluation of Post-hoc Explanationscited by 58
  20. 2022Concept Activation Regions: A Generalized Framework For Concept-Based Explanationscited by 57
  21. 2021Convolutional Dynamic Alignment Networks for Interpretable Classificationscited by 54
  22. 2022Concept-level Debugging of Part-Prototype Networkscited by 52
  23. 2022Leveraging Explanations in Interactive Machine Learning: An Overviewcited by 52
  24. 2021Entropy-based Logic Explanations of Neural Networkscited by 51
  25. 2023Learning Bottleneck Concepts in Image Classificationcited by 49
  26. 2023FunnyBirds: A Synthetic Vision Dataset for a Part-Based Analysis of Explainable AI Methodscited by 49
  27. 2023Adversarial attacks and defenses in explainable artificial intelligence: A surveycited by 47
  28. 2021A Framework for Learning Ante-hoc Explainable Models via Conceptscited by 45
  29. 2021Interpretable Models for Granger Causality Using Self-explaining Neural Networkscited by 44
  30. 2021SelfExplain: A Self-Explaining Architecture for Neural Text Classifierscited by 44
  31. 2021This looks more like that: Enhancing Self-Explaining Models by Prototypical Relevance Propagationcited by 43
  32. 2023Causal Discovery from Temporal Data: An Overview and New Perspectivescited by 43
  33. 2021Evaluating Robustness of Counterfactual Explanationscited by 41
  34. 2021Shapley Explanation Networkscited by 41
  35. 2023Learning to Receive Help: Intervention-Aware Concept Embedding Modelscited by 41
  36. 2021Robust Machine Learning Systems: Challenges, Current Trends, Perspectives, and the Road Aheadcited by 40
  37. 2022Fairness via Explanation Quality: Evaluating Disparities in the Quality of Post hoc Explanationscited by 40
  38. 2022Connecting Algorithmic Research and Usage Contexts: A Perspective of Contextualized Evaluation for Explainable AIcited by 40

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