--- title: "Safety Research — papers" description: "Safety Research: 168 papers of the Elamur Research library · page 1 of 3 · 1–25, the most cited first — our summary of each, its ideas, references and…" canonical: "https://research.elamur.ai/free/en/shelves/social-sciences/safety-research/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/shelves/social-sciences/safety-research/) [home](https://md.research.elamur.ai/free/en/) / [shelves](https://md.research.elamur.ai/free/en/shelves/) / [Social Sciences](https://md.research.elamur.ai/free/en/shelves/social-sciences/) / Safety Research # 🤝 Safety Research 168 papers · page 1 of 3 · 1–25 · the most cited first · [all shelves](https://md.research.elamur.ai/free/en/shelves/) Safety Research is a subfield of Social Sciences in the OpenAlex classification of science. The topics most of its papers here are on: «Ethics and Social Impacts of AI», «Academic integrity and plagiarism», «Disability Rights and Representation». ## Papers **1** [2](https://md.research.elamur.ai/free/en/shelves/social-sciences/safety-research/page/2/) [3](https://md.research.elamur.ai/free/en/shelves/social-sciences/safety-research/page/3/) [next →](https://md.research.elamur.ai/free/en/shelves/social-sciences/safety-research/page/2/) 1. [**Chain-of-Thought Prompting Elicits Reasoning in Large Language Models**Jason Wei, Xuezhi Wang, Dale Schuurmans, et al.*2022 · 11,278 citations*](https://md.research.elamur.ai/free/en/papers/wei-2022-chain-thought-prompting-elicits-reasoning/) 2. [**Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead**Cynthia Rudin*2018 · 10,427 citations*](https://md.research.elamur.ai/free/en/papers/rudin-2018-stop-explaining-black-box/) 3. [**A Survey on Bias and Fairness in Machine Learning**Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, et al.*2019 · 5,622 citations*](https://md.research.elamur.ai/free/en/papers/mehrabi-2019-survey-bias-fairness-machine-learning/) 4. [**Explanation in Artificial Intelligence: Insights from the Social Sciences**Tim Miller*2017 · 5,362 citations*](https://md.research.elamur.ai/free/en/papers/miller-2017-explanation-artificial-intelligence-insights-social/) 5. [**Towards A Rigorous Science of Interpretable Machine Learning**Finale Doshi-Velez, Been Kim*2017 · 3,202 citations*](https://md.research.elamur.ai/free/en/papers/doshi-velez-2017-towards-rigorous-science-interpretable-machine/) 6. [**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/) 7. [**ReAct: Synergizing Reasoning and Acting in Language Models**Shunyu Yao, Jeffrey Zhao, Dian Yu, et al.*2022 · 2,493 citations*](https://md.research.elamur.ai/free/en/papers/yao-2022-react-synergizing-reasoning-acting-language/) 8. [**Equality of Opportunity in Supervised Learning**Moritz Hardt, Eric Price, Nathan Srebro*2016 · 2,224 citations*](https://md.research.elamur.ai/free/en/papers/hardt-2016-equality-opportunity-supervised-learning/) 9. [**Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings**Tolga Bolukbasi, Kai-Wei Chang, James Zou, et al.*2016 · 2,038 citations*](https://md.research.elamur.ai/free/en/papers/bolukbasi-2016-man-computer-programmer-woman-homemaker/) 10. [**Language (Technology) is Power: A Critical Survey of “Bias” in NLP**Su Lin Blodgett, Solon Barocas, Hal Daumé III, et al.*2020 · 1,700 citations*](https://md.research.elamur.ai/free/en/papers/blodgett-2020-language-technology-is-power-critical/) 11. [**RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models**Samuel Gehman, Suchin Gururangan, Maarten Sap, et al.*2020 · 1,277 citations*](https://md.research.elamur.ai/free/en/papers/gehman-2020-realtoxicityprompts-evaluating-neural-toxic-degeneration/) 12. [**Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods**Jieyu Zhao, Tianlu Wang, Mark Yatskar, et al.*2018 · 1,186 citations*](https://md.research.elamur.ai/free/en/papers/zhao-2018-gender-bias-coreference-resolution-evaluation/) 13. [**The Risk of Racial Bias in Hate Speech Detection**Maarten Sap, Dallas Card, Saadia Gabriel, et al.*2019 · 1,148 citations*](https://md.research.elamur.ai/free/en/papers/sap-2019-risk-racial-bias-hate-speech/) 14. [**StereoSet: Measuring stereotypical bias in pretrained language models**Moin Nadeem, Anna Bethke, Siva Reddy*2021 · 1,131 citations*](https://md.research.elamur.ai/free/en/papers/nadeem-2021-stereoset-measuring-stereotypical-bias-pretrained/) 15. [**Bias and Fairness in Large Language Models: A Survey**Isabel O. Gallegos, Ryan A. Rossi, Joe Barrow, et al.*2023 · 1,072 citations*](https://md.research.elamur.ai/free/en/papers/gallegos-2023-bias-fairness-large-language-models/) 16. [**OpenAI o1 System Card**OpenAI, Aaron Jaech, Adam Kalai, et al.*2024 · 1,065 citations*](https://md.research.elamur.ai/free/en/papers/openai-2024-openai-o1-system-card/) 17. [**Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints**Jieyu Zhao, Tianlu Wang, Mark Yatskar, et al.*2017 · 1,030 citations*](https://md.research.elamur.ai/free/en/papers/zhao-2017-men-also-like-shopping-reducing/) 18. [**Out of One, Many: Using Language Models to Simulate Human Samples**Lisa P. Argyle, Ethan C. Busby, Nancy Fulda, et al.*2022 · 942 citations*](https://md.research.elamur.ai/free/en/papers/argyle-2022-out-one-many-using-language/) 19. [**CrowS-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language Models**Nikita Nangia, Clara Vania, Rasika Bhalerao, et al.*2020 · 886 citations*](https://md.research.elamur.ai/free/en/papers/nangia-2020-crows-pairs-challenge-dataset-measuring/) 20. [**Gender Bias in Coreference Resolution**Rachel Rudinger, Jason Naradowsky, Brian Leonard, et al.*2018 · 852 citations*](https://md.research.elamur.ai/free/en/papers/rudinger-2018-gender-bias-coreference-resolution/) 21. [**On Measuring Social Biases in Sentence Encoders**Chandler May, Alex Wang, Shikha Bordia, et al.*2019 · 761 citations*](https://md.research.elamur.ai/free/en/papers/may-2019-measuring-social-biases-sentence-encoders/) 22. [**The Woman Worked as a Babysitter: On Biases in Language Generation**Emily Sheng, Kai-Wei Chang, Premkumar Natarajan, et al.*2019 · 751 citations*](https://md.research.elamur.ai/free/en/papers/sheng-2019-woman-worked-as-babysitter-biases/) 23. [**Mitigating Gender Bias in Natural Language Processing: Literature Review**Tony Sun, Andrew Gaut, Shirlyn Tang, et al.*2019 · 716 citations*](https://md.research.elamur.ai/free/en/papers/sun-2019-mitigating-gender-bias-natural-language/) 24. [**CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society**Guohao Li, Hasan Abed Al Kader Hammoud, Hani Itani, et al.*2023 · 689 citations*](https://md.research.elamur.ai/free/en/papers/li-2023-camel-communicative-agents-mind/) 25. [**Jailbroken: How Does LLM Safety Training Fail?**Alexander Wei, Nika Haghtalab, Jacob Steinhardt*2023 · 659 citations*](https://md.research.elamur.ai/free/en/papers/wei-2023-jailbroken-llm-safety-training-fail/) **1** [2](https://md.research.elamur.ai/free/en/shelves/social-sciences/safety-research/page/2/) [3](https://md.research.elamur.ai/free/en/shelves/social-sciences/safety-research/page/3/) [next →](https://md.research.elamur.ai/free/en/shelves/social-sciences/safety-research/page/2/) Updated 23 October 2026 - [← previous subfield Political Science and International Relations](https://md.research.elamur.ai/free/en/shelves/social-sciences/political-science-and-international-relations/) - [next subfield → Sociology and Political Science](https://md.research.elamur.ai/free/en/shelves/social-sciences/sociology-and-political-science/) - [↑ back to Social Sciences](https://md.research.elamur.ai/free/en/shelves/social-sciences/)