Aligning Faithful Interpretations with their Social Attribution
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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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- 2022ROSCOE: A Suite of Metrics for Scoring Step-by-Step Reasoningcited by 175
- 2022The Unreliability of Explanations in Few-shot Prompting for Textual Reasoningcited by 169
- 2022Towards Faithful Model Explanation in NLP: A Surveycited by 129
- 2021Teach Me to Explain: A Review of Datasets for Explainable Natural Language Processingcited by 126
- 2021Do Feature Attribution Methods Correctly Attribute Features?cited by 98
- 2021The Out-of-Distribution Problem in Explainability and Search Methods for Feature Importance Explanationscited by 86
- 2021Explainability of deep vision-based autonomous driving systems: Review and challengescited by 80
- 2021Contrastive Explanations for Model Interpretabilitycited by 75
- 2022Framework for Evaluating Faithfulness of Local Explanationscited by 55
- 2021ProoFVer: Natural Logic Theorem Proving for Fact Verificationcited by 52
- 2023In Search of Verifiability: Explanations Rarely Enable Complementary Performance in AI-Advised Decision Makingcited by 50
- 2021Understanding Interlocking Dynamics of Cooperative Rationalizationcited by 36
38 more
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- 2022Aligning Eyes between Humans and Deep Neural Network through Interactive Attention Alignmentcited by 27
- 2021Explain, Edit, and Understand: Rethinking User Study Design for Evaluating Model Explanationscited by 26
- 2021What to Learn, and How: Toward Effective Learning from Rationalescited by 25
- 2022Human Interpretation of Saliency-based Explanation Over Textcited by 25
- 2024Mechanistic?cited by 25
- 2021Knowledge-Grounded Self-Rationalization via Extractive and Natural Language Explanationscited by 24
- 2024Properties and Challenges of LLM-Generated Explanationscited by 24
- 2022RES: A Robust Framework for Guiding Visual Explanationcited by 22
- 2021Connecting Attributions and QA Model Behavior on Realistic Counterfactualscited by 20
- 2022Learning to Scaffold: Optimizing Model Explanations for Teachingcited by 20
- 2021SPECTRA: Sparse Structured Text Rationalizationcited by 19
- 2021Interpreting Deep Learning Models in Natural Language Processing: A Reviewcited by 19
- 2022Necessity and Sufficiency for Explaining Text Classifiers: A Case Study in Hate Speech Detectioncited by 18
- 2025A Comprehensive Survey on Self-Interpretable Neural Networkscited by 18
- 2023TextGenSHAP: Scalable Post-hoc Explanations in Text Generation with Long Documentscited by 17
- 2021The Irrationality of Neural Rationale Modelscited by 16
- 2022Diagnosing AI Explanation Methods with Folk Concepts of Behaviorcited by 15
- 2022Towards Explainable Evaluation Metrics for Natural Language Generationcited by 15
- 2022A survey on improving NLP models with human explanationscited by 15
- 2023Towards Explainable Evaluation Metrics for Machine Translationcited by 15
- 2024Towards Faithful Natural Language Explanations: A Study Using Activation Patching in Large Language Modelscited by 14
- 2022Investigating the Benefits of Free-Form Rationalescited by 13
- 2024Sparse and Structured Hopfield Networkscited by 13
- 2021On Sample Based Explanation Methods for NLP:Efficiency, Faithfulness, and Semantic Evaluationcited by 12
- 2022Making a (Counterfactual) Difference One Rationale at a Timecited by 10
- 2022How explainable are adversarially-robust CNNs?cited by 9
- 2024Intrinsic User-Centric Interpretability through Global Mixture of Expertscited by 9
- 2023ZARA: Improving Few-Shot Self-Rationalization for Small Language Modelscited by 8
- 2023Feature Interactions Reveal Linguistic Structure in Language Modelscited by 8
- 2023QA-NatVer: Question Answering for Natural Logic-based Fact Verificationcited by 8
- 2023Explaining with Contrastive Phrasal Highlighting: A Case Study in Assisting Humans to Detect Translation Differencescited by 8
- 2025Rubrik's Cube: Testing a New Rubric for Evaluating Explanations on the CUBE datasetcited by 8
- 2022A Theoretical Framework for AI Models Explainability with Application in Biomedicinecited by 7
- 2023Proto-lm: A Prototypical Network-Based Framework for Built-in Interpretability in Large Language Modelscited by 7
- 2021Diagnostics-Guided Explanation Generationcited by 6
50 most cited of 79 All 79 (JSON)
- 2026The Impact of Stochasticity on the Rashomon Effect in Machine Learning26 Sep 2026
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- 2026From Plausible to Actionable: A Position on LLM Self-Explanations17 Jul 2026
- 2026Interpretability Can Be Actionable11 May 2026
- 2025T-FIX: Text-Based Explanations with Features Interpretable to eXperts6 Nov 2025
- 2025Argument-Based Consistency in Toxicity Explanations of LLMs23 Jun 2025
- 2025Adversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean Datasets4 May 2025
- 2025Noiser: Bounded Input Perturbations for Attributing Large Language Models3 Apr 2025
- 2025Rubrik's Cube: Testing a New Rubric for Evaluating Explanations on the CUBE dataset31 Mar 2025
- 2025B-cos LM: Efficiently Transforming Pre-trained Language Models for Improved Explainability18 Feb 2025
- 2025A Comprehensive Survey on Self-Interpretable Neural Networks26 Jan 2025
- 2025Boosting Explainability through Selective Rationalization in Pre-trained Language Models3 Jan 2025
38 more
- 2024Reconciling Privacy and Explainability in High-Stakes: A Systematic Inquiry30 Dec 2024
- 2024TabVer: Tabular Fact Verification with Natural Logic2 Nov 2024
- 2024Towards Faithful Natural Language Explanations: A Study Using Activation Patching in Large Language Models18 Oct 2024
- 2024Mechanistic?7 Oct 2024
- 2024Faithfulness and the Notion of Adversarial Sensitivity in NLP Explanations26 Sep 2024
- 2024Counterfactuals As a Means for Evaluating Faithfulness of Attribution Methods in Autoregressive Language Models21 Aug 2024
- 2024Explanation Regularisation through the Lens of Attributions23 Jul 2024
- 2024Local Feature Selection without Label or Feature Leakage for Interpretable Machine Learning Predictions16 Jul 2024
- 2024Exploring the Trade-off Between Model Performance and Explanation Plausibility of Text Classifiers Using Human Rationales3 Apr 2024
- 2024Sparse and Structured Hopfield Networks21 Feb 2024
- 2024Properties and Challenges of LLM-Generated Explanations16 Feb 2024
- 2024Intrinsic User-Centric Interpretability through Global Mixture of Experts5 Feb 2024
- 2023Explaining with Contrastive Phrasal Highlighting: A Case Study in Assisting Humans to Detect Translation Differences4 Dec 2023
- 2023TextGenSHAP: Scalable Post-hoc Explanations in Text Generation with Long Documents3 Dec 2023
- 2023Proto-lm: A Prototypical Network-Based Framework for Built-in Interpretability in Large Language Models3 Nov 2023
- 2023This Reads Like That: Deep Learning for Interpretable Natural Language Processing25 Oct 2023
- 2023QA-NatVer: Question Answering for Natural Logic-based Fact Verification22 Oct 2023
- 2023Situated Natural Language Explanations27 Aug 2023
- 2023DARE: Towards Robust Text Explanations in Biomedical and Healthcare Applications5 Jul 2023
- 2023Towards Explainable Evaluation Metrics for Machine Translation22 Jun 2023
- 2023Feature Interactions Reveal Linguistic Structure in Language Models21 Jun 2023
- 2023Unsupervised Selective Rationalization with Noise Injection27 May 2023
- 2023In Search of Verifiability: Explanations Rarely Enable Complementary Performance in AI-Advised Decision Making12 May 2023
- 2023ZARA: Improving Few-Shot Self-Rationalization for Small Language Models12 May 2023
- 2022A Theoretical Framework for AI Models Explainability with Application in Biomedicine29 Dec 2022
- 2022ROSCOE: A Suite of Metrics for Scoring Step-by-Step Reasoning15 Dec 2022
- 2022Explainability of Text Processing and Retrieval Methods: A Survey14 Dec 2022
- 2022Evaluating the Faithfulness of Saliency-based Explanations for Deep Learning Models for Temporal Colour Constancy15 Nov 2022
- 2022Towards Faithful Model Explanation in NLP: A Survey22 Sep 2022
- 2022From Correlation to Causation: Formalizing Interpretable Machine Learning as a Statistical Process11 Jul 2022
- 2022RES: A Robust Framework for Guiding Visual Explanation27 Jun 2022
- 2022Explanation-based Counterfactual Retraining(XCR): A Calibration Method for Black-box Models22 Jun 2022
- 2022EiX-GNN : Concept-level eigencentrality explainer for graph neural networks7 Jun 2022
- 2022Investigating the Benefits of Free-Form Rationales25 May 2022
- 2022How explainable are adversarially-robust CNNs?25 May 2022
- 2022The Unreliability of Explanations in Few-shot Prompting for Textual Reasoning6 May 2022
- 2022Necessity and Sufficiency for Explaining Text Classifiers: A Case Study in Hate Speech Detection6 May 2022
- 2022Learning to Scaffold: Optimizing Model Explanations for Teaching22 Apr 2022
50 newest of 79 All 79 (JSON)
- 2022ROSCOE: A Suite of Metrics for Scoring Step-by-Step Reasoningcited by 175
- 2022The Unreliability of Explanations in Few-shot Prompting for Textual Reasoningcited by 169
- 2023TextGenSHAP: Scalable Post-hoc Explanations in Text Generation with Long Documentscited by 17
- 2024TabVer: Tabular Fact Verification with Natural Logiccited by 3
- 2024TabVer: Tabular Fact Verification with Natural Logic2 Nov 2024
- 2023TextGenSHAP: Scalable Post-hoc Explanations in Text Generation with Long Documents3 Dec 2023
- 2022ROSCOE: A Suite of Metrics for Scoring Step-by-Step Reasoning15 Dec 2022
- 2022The Unreliability of Explanations in Few-shot Prompting for Textual Reasoning6 May 2022
- 2022Towards Faithful Model Explanation in NLP: A Surveycited by 129
- 2021Teach Me to Explain: A Review of Datasets for Explainable Natural Language Processingcited by 126
- 2021Do Feature Attribution Methods Correctly Attribute Features?cited by 98
- 2021The Out-of-Distribution Problem in Explainability and Search Methods for Feature Importance Explanationscited by 86
- 2021Explainability of deep vision-based autonomous driving systems: Review and challengescited by 80
- 2021Contrastive Explanations for Model Interpretabilitycited by 75
- 2022Framework for Evaluating Faithfulness of Local Explanationscited by 55
- 2021ProoFVer: Natural Logic Theorem Proving for Fact Verificationcited by 52
- 2023In Search of Verifiability: Explanations Rarely Enable Complementary Performance in AI-Advised Decision Makingcited by 50
- 2021Understanding Interlocking Dynamics of Cooperative Rationalizationcited by 36
- 2021Zorro: Valid, Sparse, and Stable Explanations in Graph Neural Networkscited by 34
- 2021Is Sparse Attention more Interpretable?cited by 31
12 most cited of 73 All 73 (JSON)
- 2026The Impact of Stochasticity on the Rashomon Effect in Machine Learning26 Sep 2026
- 2026From Plausible to Actionable: A Position on LLM Self-Explanations17 Jul 2026
- 2026Interpretability Can Be Actionable11 May 2026
- 2025T-FIX: Text-Based Explanations with Features Interpretable to eXperts6 Nov 2025
- 2025Argument-Based Consistency in Toxicity Explanations of LLMs23 Jun 2025
- 2025Adversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean Datasets4 May 2025
- 2025Noiser: Bounded Input Perturbations for Attributing Large Language Models3 Apr 2025
- 2025Rubrik's Cube: Testing a New Rubric for Evaluating Explanations on the CUBE dataset31 Mar 2025
- 2025B-cos LM: Efficiently Transforming Pre-trained Language Models for Improved Explainability18 Feb 2025
- 2025A Comprehensive Survey on Self-Interpretable Neural Networks26 Jan 2025
- 2025Boosting Explainability through Selective Rationalization in Pre-trained Language Models3 Jan 2025
- 2024Reconciling Privacy and Explainability in High-Stakes: A Systematic Inquiry30 Dec 2024
12 newest of 73 All 73 (JSON)