Schiebinger, Londa

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

AI can be sexist and racist — it’s time to make it fair

Zou, J., & Schiebinger, L. (2018) · Nature

Ethics and Social Impacts of AIExplainable Artificial Intelligence (XAI)

Cited by 765

Gender diversity leads to better science

Nielsen, M. W., Alegria, S., Börjeson, L., Etzkowitz, H., Falk-Krzesinski, H. J., Joshi, A., Leahey, E., Smith-Doerr, L., Woolley, A. W., & Schiebinger, L. (2017) · Proceedings of the National Academy of Sciences

Career Development and DiversityGender Diversity and Inequality

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Gender-related variables for health research

Nielsen, M. W., Stefanick, M. L., Peragine, D., Neilands, T. B., Ioannidis, J. P. A., Pilote, L., Prochaska, J. J., Cullen, M. R., Einstein, G., Klinge, I., et al. (2021) · Biology of Sex Differences

Sex and Gender in HealthcareGender, Health, and Social Inequality

Cited by 238

Sex inclusion in basic research drives discovery

Klein, S. L., Schiebinger, L., Stefanick, M. L., Cahill, L., Danska, J., de Vries, G. J., Kibbe, M. R., McCarthy, M. M., Mogil, J. S., Woodruff, T. K., et al. (2015) · Proceedings of the National Academy of Sciences

Sex and Gender in HealthcareDiversity and Career in Medicine

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Ensuring that biomedical AI benefits diverse populations

Zou, J., & Schiebinger, L. (2021) · EBioMedicine

Artificial Intelligence in Healthcare and EducationEthics and Social Impacts of AI

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