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Aligning Faithful Interpretations with their Social Attribution

arXiv 2006.01067 ↗First posted 1 Jun 2020cs.CL cs.AIcited by 79

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Abstract (arXiv)

We find that the requirement of model interpretations to be faithful is vague and incomplete. With interpretation by textual highlights as a case-study, we present several failure cases. Borrowing concepts from social science, we identify that the problem is a misalignment between the causal chain of decisions (causal attribution) and the attribution of human behavior to the interpretation (social attribution). We re-formulate faithfulness as an accurate attribution of causality to the model, and introduce the concept of aligned faithfulness: faithful causal chains that are aligned with their expected social behavior. The two steps of causal attribution and social attribution together complete the process of explaining behavior. With this formalization, we characterize various failures of misaligned faithful highlight interpretations, and propose an alternative causal chain to remedy the issues. Finally, we implement highlight explanations of the proposed causal format using contrastive explanations.

Key facts. Cited by 79 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Jun 2020.

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Citing papers per year: 2021: 22; 2022: 21; 2023: 12; 2024: 12; 2025: 8; 2026: 4.

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  1. 2022ROSCOE: A Suite of Metrics for Scoring Step-by-Step Reasoningcited by 175
  2. 2022The Unreliability of Explanations in Few-shot Prompting for Textual Reasoningcited by 169
  3. 2022Towards Faithful Model Explanation in NLP: A Surveycited by 129
  4. 2021Teach Me to Explain: A Review of Datasets for Explainable Natural Language Processingcited by 126
  5. 2021Do Feature Attribution Methods Correctly Attribute Features?cited by 98
  6. 2021The Out-of-Distribution Problem in Explainability and Search Methods for Feature Importance Explanationscited by 86
  7. 2021Explainability of deep vision-based autonomous driving systems: Review and challengescited by 80
  8. 2021Contrastive Explanations for Model Interpretabilitycited by 75
  9. 2022Framework for Evaluating Faithfulness of Local Explanationscited by 55
  10. 2021ProoFVer: Natural Logic Theorem Proving for Fact Verificationcited by 52
  11. 2023In Search of Verifiability: Explanations Rarely Enable Complementary Performance in AI-Advised Decision Makingcited by 50
  12. 2021Understanding Interlocking Dynamics of Cooperative Rationalizationcited by 36
38 more
  1. 2021Zorro: Valid, Sparse, and Stable Explanations in Graph Neural Networkscited by 34
  2. 2021Is Sparse Attention more Interpretable?cited by 31
  3. 2021Exploring the Efficacy of Automatically Generated Counterfactuals for Sentiment Analysiscited by 30
  4. 2022Aligning Eyes between Humans and Deep Neural Network through Interactive Attention Alignmentcited by 27
  5. 2021Explain, Edit, and Understand: Rethinking User Study Design for Evaluating Model Explanationscited by 26
  6. 2021What to Learn, and How: Toward Effective Learning from Rationalescited by 25
  7. 2022Human Interpretation of Saliency-based Explanation Over Textcited by 25
  8. 2024Mechanistic?cited by 25
  9. 2021Knowledge-Grounded Self-Rationalization via Extractive and Natural Language Explanationscited by 24
  10. 2024Properties and Challenges of LLM-Generated Explanationscited by 24
  11. 2022RES: A Robust Framework for Guiding Visual Explanationcited by 22
  12. 2021Connecting Attributions and QA Model Behavior on Realistic Counterfactualscited by 20
  13. 2022Learning to Scaffold: Optimizing Model Explanations for Teachingcited by 20
  14. 2021SPECTRA: Sparse Structured Text Rationalizationcited by 19
  15. 2021Interpreting Deep Learning Models in Natural Language Processing: A Reviewcited by 19
  16. 2022Necessity and Sufficiency for Explaining Text Classifiers: A Case Study in Hate Speech Detectioncited by 18
  17. 2025A Comprehensive Survey on Self-Interpretable Neural Networkscited by 18
  18. 2023TextGenSHAP: Scalable Post-hoc Explanations in Text Generation with Long Documentscited by 17
  19. 2021The Irrationality of Neural Rationale Modelscited by 16
  20. 2022Diagnosing AI Explanation Methods with Folk Concepts of Behaviorcited by 15
  21. 2022Towards Explainable Evaluation Metrics for Natural Language Generationcited by 15
  22. 2022A survey on improving NLP models with human explanationscited by 15
  23. 2023Towards Explainable Evaluation Metrics for Machine Translationcited by 15
  24. 2024Towards Faithful Natural Language Explanations: A Study Using Activation Patching in Large Language Modelscited by 14
  25. 2022Investigating the Benefits of Free-Form Rationalescited by 13
  26. 2024Sparse and Structured Hopfield Networkscited by 13
  27. 2021On Sample Based Explanation Methods for NLP:Efficiency, Faithfulness, and Semantic Evaluationcited by 12
  28. 2022Making a (Counterfactual) Difference One Rationale at a Timecited by 10
  29. 2022How explainable are adversarially-robust CNNs?cited by 9
  30. 2024Intrinsic User-Centric Interpretability through Global Mixture of Expertscited by 9
  31. 2023ZARA: Improving Few-Shot Self-Rationalization for Small Language Modelscited by 8
  32. 2023Feature Interactions Reveal Linguistic Structure in Language Modelscited by 8
  33. 2023QA-NatVer: Question Answering for Natural Logic-based Fact Verificationcited by 8
  34. 2023Explaining with Contrastive Phrasal Highlighting: A Case Study in Assisting Humans to Detect Translation Differencescited by 8
  35. 2025Rubrik's Cube: Testing a New Rubric for Evaluating Explanations on the CUBE datasetcited by 8
  36. 2022A Theoretical Framework for AI Models Explainability with Application in Biomedicinecited by 7
  37. 2023Proto-lm: A Prototypical Network-Based Framework for Built-in Interpretability in Large Language Modelscited by 7
  38. 2021Diagnostics-Guided Explanation Generationcited by 6

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