Prabhakaran, Vinodkumar

PaLM: Scaling Language Modeling with Pathways

Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H. W., Sutton, C., Gehrmann, S., et al. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 2,590

LaMDA: Language Models for Dialog Applications

Thoppilan, R., De Freitas, D., Hall, J., Shazeer, N., Kulshreshtha, A., Cheng, H.-T., Jin, A., Bos, T., Baker, L., Du, Y., et al. (2022) · arXiv

Speech and dialogue systemsTopic Modeling

Cited by 778

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

Power to the People? Opportunities and Challenges for Participatory AI

Birhane, A., Isaac, W., Prabhakaran, V., Diaz, M., Elish, M. C., Gabriel, I., & Mohamed, S. (2022) · Equity and Access in Algorithms Mechanisms and Optimization

Ethics and Social Impacts of AIExplainable Artificial Intelligence (XAI)

Cited by 330

Social Biases in NLP Models as Barriers for Persons with Disabilities

Hutchinson, B., Prabhakaran, V., Denton, E., Webster, K., Zhong, Y., & Denuyl, S. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Ethics and Social Impacts of AIHate Speech and Cyberbullying Detection

Cited by 274

Social Biases in NLP Models as Barriers for Persons with Disabilities

Hutchinson, B., Prabhakaran, V., Denton, E., Webster, K., Zhong, Y., & Denuyl, S. (2020) · arXiv

Ethics and Social Impacts of AIHate Speech and Cyberbullying Detection

Cited by 274

Re-imagining Algorithmic Fairness in India and Beyond

Sambasivan, N., Arnesen, E., Hutchinson, B., Doshi, T., & Prabhakaran, V. (2021) · Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency

Ethics and Social Impacts of AIExplainable Artificial Intelligence (XAI)

Cited by 228

Perturbation Sensitivity Analysis to Detect Unintended Model Biases

Prabhakaran, V., Hutchinson, B., & Mitchell, M. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Topic ModelingNatural Language Processing Techniques

Cited by 115

CrowdWorkSheets: Accounting for Individual and Collective Identities Underlying Crowdsourced Dataset Annotation

Díaz, M., Kivlichan, I., Rosen, R., Baker, D., Amironesei, R., Prabhakaran, V., & Denton, R. (2022) · 2022 ACM Conference on Fairness Accountability and Transparency

Ethics and Social Impacts of AIMobile Crowdsensing and Crowdsourcing

Cited by 72

Unintended machine learning biases as social barriers for persons with disabilitiess

Hutchinson, B., Prabhakaran, V., Denton, R., Webster, K., Zhong, Y., & Denuyl, S. (2020) · ACM SIGACCESS Accessibility and Computing

Ethics and Social Impacts of AIDisability Rights and Representation

Cited by 49

Cultural Incongruencies in Artificial Intelligence

Prabhakaran, V., Qadri, R., & Hutchinson, B. (2022) · arXiv

Ethics and Social Impacts of AIArtificial Intelligence Applications

Cited by 42

Humanlike AI Design Increases Anthropomorphism but Yields Divergent Outcomes on Engagement and Trust Globally

Schimmelpfennig, R., Díaz, M., Prabhakaran, V., & Davani, A. (2025) · arXiv

Social Robot Interaction and HRIAI in Service Interactions

Cited by 2