Jurafsky, Dan

On the Opportunities and Risks of Foundation Models

Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M. S., Bohg, J., Bosselut, A., Brunskill, E., et al. (2021) · arXiv

Ethics and Social Impacts of AIArtificial Intelligence Applications

Cited by 2,676

Word embeddings quantify 100 years of gender and ethnic stereotypes

Garg, N., Schiebinger, L., Jurafsky, D., & Zou, J. (2018) · Proceedings of the National Academy of Sciences

Ethics and Social Impacts of AIComputational and Text Analysis Methods

Cited by 1,065

Racial disparities in automated speech recognition

Koenecke, A., Nam, A., Lake, E., Nudell, J., Quartey, M., Mengesha, Z., Toups, C., Rickford, J. R., Jurafsky, D., & Goel, S. (2020) · Proceedings of the National Academy of Sciences

Speech Recognition and SynthesisEthics and Social Impacts of AI

Cited by 765

Assessing the potential of GPT-4 to perpetuate racial and gender biases in health care: a model evaluation study

Zack, T., Lehman, E., Suzgun, M., Rodriguez, J. A., Celi, L. A., Gichoya, J., Jurafsky, D., Szolovits, P., Bates, D. W., Abdulnour, R.-E. E., et al. (2024) · The Lancet Digital Health

Artificial Intelligence in Healthcare and EducationEthics and Social Impacts of AI

Cited by 510

Language from police body camera footage shows racial disparities in officer respect

Voigt, R., Camp, N. P., Prabhakaran, V., Hamilton, W. L., Hetey, R. C., Griffiths, C. M., Jurgens, D., Jurafsky, D., & Eberhardt, J. L. (2017) · Proceedings of the National Academy of Sciences

Policing Practices and PerceptionsLanguage, Discourse, Communication Strategies

Cited by 407

Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning

Henderson, P., Hu, J., Romoff, J., Brunskill, E., Jurafsky, D., & Pineau, J. (2020) · arXiv

Green IT and SustainabilityEnergy Efficiency in Computing

Cited by 393

Easily Accessible Text-to-Image Generation Amplifies Demographic Stereotypes at Large Scale

Bianchi, F., Kalluri, P., Durmus, E., Ladhak, F., Cheng, M., Nozza, D., Hashimoto, T., Jurafsky, D., Zou, J., & Caliskan, A. (2023) · 2023 ACM Conference on Fairness, Accountability, and Transparency

Ethics and Social Impacts of AIHate Speech and Cyberbullying Detection

Cited by 342

Sharp nearby, fuzzy far away: How neural language models use context

Khandelwal, U., He, H., Qi, P., & Jurafsky, D. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Topic ModelingNatural Language Processing Techniques

Cited by 303

Sharp Nearby, Fuzzy Far Away: How Neural Language Models Use Context

Khandelwal, U., He, H., Qi, P., & Jurafsky, D. (2018) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 302

Generalization through Memorization: Nearest Neighbor Language Models

Khandelwal, U., Levy, O., Jurafsky, D., Zettlemoyer, L., & Lewis, M. (2019) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 285

AI generates covertly racist decisions about people based on their dialect

Hofmann, V., Kalluri, P. R., Jurafsky, D., & King, S. (2024) · Nature

Ethics and Social Impacts of AILinguistic Variation and Morphology

Cited by 233

Measuring the Evolution of a Scientific Field through Citation Frames

Jurgens, D., Kumar, S., Hoover, R., McFarland, D., & Jurafsky, D. (2018) · Transactions of the Association for Computational Linguistics

scientometrics and bibliometrics researchWeb visibility and informetrics

Cited by 212

Sycophantic AI decreases prosocial intentions and promotes dependence

Cheng, M., Lee, C., Khadpe, P., Yu, S., Han, D., & Jurafsky, D. (2026) · Science

Ethics and Social Impacts of AISocial Robot Interaction and HRI

Cited by 185

Embedding Logical Queries on Knowledge Graphs

Hamilton, W. L., Bajaj, P., Zitnik, M., Jurafsky, D., & Leskovec, J. (2018) · arXiv

Advanced Graph Neural NetworksTopic Modeling

Cited by 176

The NXT-format Switchboard Corpus: a rich resource for investigating the syntax, semantics, pragmatics and prosody of dialogue

Calhoun, S., Carletta, J., Brenier, J. M., Mayo, N., Jurafsky, D., Steedman, M., & Beaver, D. (2010) · Language Resources and Evaluation

Linguistic Studies and Language AcquisitionSpeech and dialogue systems

Cited by 174

Which words are hard to recognize? Prosodic, lexical, and disfluency factors that increase speech recognition error rates

Goldwater, S., Jurafsky, D., & Manning, C. D. (2010) · Speech Communication

Speech Recognition and SynthesisPhonetics and Phonology Research

Cited by 166

Nearest Neighbor Machine Translation

Khandelwal, U., Fan, A., Jurafsky, D., Zettlemoyer, L., & Lewis, M. (2020) · arXiv

Natural Language Processing TechniquesTopic Modeling

Cited by 165

Analyzing Polarization in Social Media: Method and Application to Tweets on 21 Mass Shootings

Demszky, D., Garg, N., Voigt, R., Zou, J., Shapiro, J., Gentzkow, M., & Jurafsky, D. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

Sentiment Analysis and Opinion MiningHate Speech and Cyberbullying Detection

Cited by 164

Improving Factual Completeness and Consistency of Image-to-Text Radiology Report Generation

Miura, Y., Zhang, Y., Tsai, E., Langlotz, C., & Jurafsky, D. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Multimodal Machine Learning ApplicationsArtificial Intelligence in Healthcare and Education

Cited by 149

Utility is in the Eye of the User: A Critique of NLP Leaderboards

Ethayarajh, K., & Jurafsky, D. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Topic ModelingNatural Language Processing Techniques

Cited by 147

Automatic detection of Incoherent speech for diagnosing schizophrenia

Iter, D., Yoon, J., & Jurafsky, D. (2018) · Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic

Mental Health via WritingSchizophrenia research and treatment

Cited by 115

With Little Power Comes Great Responsibility

Card, D., Henderson, P., Khandelwal, U., Jia, R., Mahowald, K., & Jurafsky, D. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Natural Language Processing TechniquesTopic Modeling

Cited by 84

Pile of Law: Learning Responsible Data Filtering from the Law and a 256GB Open-Source Legal Dataset

Henderson, P., Krass, M., Zheng, L., Guha, N., Manning, C. D., Jurafsky, D., & Ho, D. (2022) · Advances in Neural Information Processing Systems 35

Artificial Intelligence in LawEthics and Social Impacts of AI

Cited by 62

Picking on the Same Person: Does Algorithmic Monoculture lead to Outcome Homogenization?

Bommasani, R., Creel, K. A., Kumar, A., Jurafsky, D., & Liang, P. (2022) · arXiv

Ethics and Social Impacts of AIRecommender Systems and Techniques

Cited by 60