# Emily M. Bender Emily Menon Bender (born 1973) is an American linguist and professor at the University of Washington, specializing in computational linguistics and natural language processing, known for her work on AI ethics and large language model risks. Emily Menon Bender (born 1973) is an American linguist and professor at the University of Washington, where she directs its Computational Linguistics Laboratory. She specializes in computational linguistics and natural language processing, with a focus on the intersection of linguistic theory and [machine-learning](https://www.wikiprompt.org/wiki/machine-learning) systems. Bender is known for her critical analyses of [large-language-model](https://www.wikiprompt.org/wiki/large-language-model) risks and for advocating ethical practices in the field. Bender has published extensively on the dangers of large language models and on ethics in natural language processing. She co-authored the 2025 book *The AI Con: How to Fight Big Tech's Hype and Create the Future We Want*, which critiques industry narratives around [artificial-intelligence](https://www.wikiprompt.org/wiki/artificial-intelligence). Her work has influenced debates on AI accountability and the limitations of statistical text generation. ## Education Bender earned an AB in Linguistics from the University of California, Berkeley in 1995. She received her MA from Stanford University in 1997 and her PhD from Stanford in 2000, completing a dissertation on syntactic variation and linguistic competence in African American Vernacular English (AAVE). Her doctoral advisors were Tom Wasow and Penelope Eckert, both prominent figures in syntax and sociolinguistics. ## Career and research Before joining the University of Washington, Bender held positions at Stanford University and UC Berkeley, and worked in industry at YY Technologies. She has been faculty at the University of Washington since 2003, where she serves as professor in the Department of Linguistics, adjunct professor in the Department of Computer Science and Engineering, faculty director of the Master of Science in Computational Linguistics, and director of the Computational Linguistics Laboratory. She holds the Howard and Frances Nostrand Endowed Professorship. Bender served as president of the Association for Computational Linguistics in 2024. She was elected a Fellow of the American Association for the Advancement of Science in 2022, recognizing her contributions to computational linguistics and AI ethics. ### Contributions Bender has published research on the linguistic structures of Japanese, Chintang, Mandarin, Wambaya, American Sign Language, and English. She constructed the LinGO Grammar Matrix, an open-source starter kit for developing broad-coverage precision Head-driven Phrase Structure Grammar (HPSG) grammars, which has been used by researchers worldwide to build grammars for diverse languages. In 2013, she published *Linguistic Fundamentals for Natural Language Processing: 100 Essentials from Morphology and Syntax*, followed by *Linguistic Fundamentals for Natural Language Processing II: 100 Essentials from Semantics and Pragmatics* in 2019, co-authored with Alex Lascarides. Both books explain basic linguistic principles in accessible terms for NLP practitioners, bridging the gap between linguistics and computational methods. In 2021, Bender presented the paper "On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜" at the ACM Conference on Fairness, Accountability, and Transparency, co-authored with Google researcher Timnit Gebru and others. The paper discussed ethical issues in building NLP systems using machine learning from large text corpora, including environmental costs, biases, and the limits of statistical learning. Google attempted to block its publication, leading to a sequence of events that culminated in Gebru's departure from the company, the details of which remain disputed. The paper became a landmark in AI ethics discourse. Since then, Bender has invested efforts in popularizing AI ethics and has taken a public stance against hype surrounding [generative-ai](https://www.wikiprompt.org/wiki/generative-ai) and [transformer](https://www.wikiprompt.org/wiki/transformer)-based models. She argues that many claims about machine understanding are overstated and that researchers should be more cautious in their assertions. ### The Bender Rule The Bender Rule, which originated from a question Bender repeatedly asked at research talks, advises computational scholars to "always name the language you're working with." This principle highlights the tendency of NLP research to default to English and encourages explicit acknowledgment of the linguistic scope of any study, promoting reproducibility and cross-linguistic validity. ### Form versus meaning Bender draws a distinction between linguistic form and linguistic meaning. Form refers to the structure of language, such as syntax and morphology, while meaning refers to the ideas that language represents. In a 2020 paper, she argued that machine learning models for NLP trained only on form, without connection to meaning, cannot meaningfully understand language. Consequently, she has argued that tools like [chatgpt](https://www.wikiprompt.org/wiki/chatgpt) have no way to meaningfully understand the text they process or generate, as they operate purely on statistical patterns of form. This perspective challenges the notion that [neural-network](https://www.wikiprompt.org/wiki/neural-network) models achieve true comprehension, and it has informed her critiques of industry claims about AI capabilities. She emphasizes that such systems are essentially "stochastic parrots" that regurgitate patterns from training data without semantic grounding. ## Published books Bender has authored or co-authored several influential books. Her 2000 dissertation, *Syntactic Variation and Linguistic Competence: The Case of AAVE Copula Absence*, was published by Stanford University. In 2003, she co-authored *Syntactic Theory: A Formal Introduction* with Ivan Sag and Tom Wasow, published by the Center for the Study of Language and Information. Her 2013 and 2019 volumes on linguistic fundamentals for NLP, published by Springer, are widely used in the field. In 2025, she co-authored *The AI Con* with Alex Hanna, published by Harper, which critiques big tech's AI hype and proposes alternative futures. ## Published articles Bender has published numerous articles in journals and conference proceedings. Notable works include "The Syntax of Mandarin Bă: Reconsidering the Verbal Analysis" (2000) in the *Journal of East Asian Linguistics*, and "The Grammar Matrix: An open-source starter-kit for the rapid development of cross-linguistically consistent broad-coverage precision grammars" (2002) with Dan Flickinger and Stephan Oepen. She also co-authored papers on Japanese processing, interlinear glossed text tools, and multilingual databases. Her 2021 paper on stochastic parrots remains her most cited work, appearing in the proceedings of the ACM conference. ## References [References omitted in this summary.] ## External links Personal page at University of Washington; Faculty page at University of Washington; Article by Emily Bender in The Linguist List's Famous Linguists series. --- Source: https://www.wikiprompt.org/wiki/emily-m-bender License: CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0/) Last updated: 2026-09-08T15:34:51.22949+00:00