References
Bukavina L, Sindhani M, Joshi SS, et al. What’s in a name? An analysis of gender-based acronyms in clinical trials. Ann Surg Oncol. 2023;30(5):2594–2596. https://doi.org/10.1245/s10434-023-13253-5.
Caliskan A, Ajay PP, Charlesworth T, Wolfe R, Banaji MR. Gender bias in word embeddings: a comprehensive analysis of frequency, syntax, and semantics. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES '22). Association for Computing Machinery, New York, NY; 2022. pp. 156–170. https://doi.org/10.1145/3514094.3534162.
Edwards B. Victor/Victoria. London: Peerford Limited, Arista Management A. G., Blake Edwards Entertainment; 1982.
Acknowledgment
Funding for this research was provided by NCI/NIH Grant No. P30CA006927.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Carlisle, B.G., Coffman, D.L., Egleston, B.L. et al. Training Artificial Intelligence on a Gender-Biased Virtual World can Result in Biased Conclusions. Ann Surg Oncol 30, 5282–5283 (2023). https://doi.org/10.1245/s10434-023-13557-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1245/s10434-023-13557-6