--- title: "Who cites Towards A Rigorous Science of Interpretable…" description: "200 papers of the Elamur Research library cite «Towards A Rigorous Science of Interpretable Machine Learning», found in their bibliographies; the most…" canonical: "https://research.elamur.ai/free/en/papers/doshi-velez-2017-towards-rigorous-science-interpretable-machine/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/doshi-velez-2017-towards-rigorous-science-interpretable-machine/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Towards A Rigorous Science of Interpretable…](https://md.research.elamur.ai/free/en/papers/doshi-velez-2017-towards-rigorous-science-interpretable-machine/) / cited by # Who cites «Towards A Rigorous Science of Interpretable Machine Learning» 200 papers of our library cite it (of the 140,884 whose bibliographies we read) · 15 with a page here, the most cited first · [← the paper](https://md.research.elamur.ai/free/en/papers/doshi-velez-2017-towards-rigorous-science-interpretable-machine/) 1. [**On the Opportunities and Risks of Foundation Models**Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, et al.*2021 · 3,044 citations*](https://md.research.elamur.ai/free/en/papers/bommasani-2021-opportunities-risks-foundation-models/) 2. [**Attention is not not Explanation**Sarah Wiegreffe, Yuval Pinter*2019 · 1,192 citations*](https://md.research.elamur.ai/free/en/papers/wiegreffe-2019-attention-is-not-not-explanation/) 3. [**ERASER: A Benchmark to Evaluate Rationalized NLP Models**Jay DeYoung, Sarthak Jain, Nazneen Fatema Rajani, et al.*2020 · 820 citations*](https://md.research.elamur.ai/free/en/papers/deyoung-2020-eraser-benchmark-evaluate-rationalized-nlp/) 4. [**Explainability for Large Language Models: A Survey**Haiyan Zhao, Hanjie Chen, Fan Yang, et al.*2023 · 785 citations*](https://md.research.elamur.ai/free/en/papers/zhao-2023-explainability-large-language-models-survey/) 5. [**Towards Faithfully Interpretable NLP Systems: How Should We Define and Evaluate Faithfulness?**Alon Jacovi, Yoav Goldberg*2020 · 747 citations*](https://md.research.elamur.ai/free/en/papers/jacovi-2020-faithfully-interpretable-nlp-systems-how/) 6. [**Analysis Methods in Neural Language Processing: A Survey**Yonatan Belinkov, James Glass*2019 · 735 citations*](https://md.research.elamur.ai/free/en/papers/belinkov-2019-analysis-methods-neural-language-processing/) 7. [**Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Prompting**Miles Turpin, Julian Michael, Ethan Perez, et al.*2023 · 568 citations*](https://md.research.elamur.ai/free/en/papers/turpin-2023-language-models-don-t-always/) 8. [**Neural Legal Judgment Prediction in English**Ilias Chalkidis, Ion Androutsopoulos, Nikolaos Aletras*2019 · 558 citations*](https://md.research.elamur.ai/free/en/papers/chalkidis-2019-neural-legal-judgment-prediction-english/) 9. [**Ethical and social risks of harm from Language Models**Laura Weidinger, John Mellor, Maribeth Rauh, et al.*2021 · 421 citations*](https://md.research.elamur.ai/free/en/papers/weidinger-2021-ethical-social-risks-harm-language/) 10. [**Learning to Faithfully Rationalize by Construction**Sarthak Jain, Sarah Wiegreffe, Yuval Pinter, et al.*2020 · 222 citations*](https://md.research.elamur.ai/free/en/papers/jain-2020-learning-faithfully-rationalize-construction/) 11. [**Measuring Association Between Labels and Free-Text Rationales**Sarah Wiegreffe, Ana Marasović, Noah A. Smith*2021 · 215 citations*](https://md.research.elamur.ai/free/en/papers/wiegreffe-2021-measuring-association-between-labels-free/) 12. [**Towards Faithful Model Explanation in NLP: A Survey**Qing Lyu, Marianna Apidianaki, Chris Callison-Burch*2024 · 194 citations*](https://md.research.elamur.ai/free/en/papers/lyu-2024-faithful-model-explanation-nlp-survey/) 13. [**Explaining Black Box Predictions and Unveiling Data Artifacts through Influence Functions**Xiaochuang Han, Byron C. Wallace, Yulia Tsvetkov*2020 · 180 citations*](https://md.research.elamur.ai/free/en/papers/han-2020-explaining-black-box-predictions-unveiling/) 14. [**FLUTE: Figurative Language Understanding through Textual Explanations**Tuhin Chakrabarty, Arkadiy Saakyan, Debanjan Ghosh, et al.*2022 · 131 citations*](https://md.research.elamur.ai/free/en/papers/chakrabarty-2022-flute-figurative-language-understanding-through/) 15. [**Interpretability for Language Learners Using Example-Based Grammatical Error Correction**Masahiro Kaneko, Sho Takase, Ayana Niwa, et al.*2022 · 47 citations*](https://md.research.elamur.ai/free/en/papers/kaneko-2022-interpretability-language-learners-example-based/) 185 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/doshi-velez-2017-towards-rigorous-science-interpretable-machine/) — Towards A Rigorous Science of Interpretable Machine Learning