Gisselbaek, Mia ; Division of Anesthesiology, Department of Anesthesiology, Clinical Pharmacology, Intensive Care and Emergency Medicine, Geneva University Hospitals and Faculty of Medicine, Geneva, Switzerland
Köselerli, Ekin ; Department of Anesthesiology and ICU, University of Ankara School of Medicine, Ankara, Turkey
Suppan, Mélanie ; Division of Anesthesiology, Department of Anesthesiology, Clinical Pharmacology, Intensive Care and Emergency Medicine, Geneva University Hospitals and Faculty of Medicine, Geneva, Switzerland
Minsart, Laurens; Department of Anesthesia, Antwerp University Hospital (UZA), Edegem, Belgium
Meco, Basak C ; Department of Anesthesiology and ICU, University of Ankara School of Medicine, Ankara, Turkey, Ankara University Brain Research Center, Ankara, Turkey
Seidel, Laurence ; Université de Liège - ULiège > Département des sciences de la santé publique
Albert, Adelin ; Université de Liège - ULiège > Département des sciences de la santé publique
Barreto Chang, Odmara L ; Department of Anesthesia and Perioperative Care, University of California San Francisco, San Francisco, CA, USA
Berger-Estilita, Joana; Institute for Medical Education, University of Bern, Bern, Switzerland, CINTESIS@RISE, Centre for Health Technology and Services Research, Faculty of Medicine, University of Porto, Porto, Portugal
Saxena, Sarah ; Université Libre de Bruxelles, Brussels, Belgium. Electronic address: Sarah.saxena@ulb.ac.be
Language :
English
Title :
Gender bias in images of anaesthesiologists generated by artificial intelligence.
Bellman, R., Introduction to Artificial Intelligence: Can Computers Think?. 1978, Boyd & Fraser Publishing Company, San Francisco.
Ali, R., Tang, O.Y., Connolly, I.D., et al. Demographic representation in 3 leading artificial intelligence text-to-image generators. JAMA Surg 159 (2024), 87–95.
Ma, D.S., Correll, J., Wittenbrink, B., The Chicago face database: a free stimulus set of faces and norming data. Behav Res Methods 47 (2015), 1122–1135.
Berger-Estilita, J., Leitl, J., Vacas, S., Neskovic, V., Stuber, F., Zdravkovic, M., Welfare practices for anaesthesiology trainees in Europe: a descriptive cross-sectional survey study. Eur J Anaesthesiol 40 (2023), 105–112.
Agarwal, S., El-Boghdadly, K., Position statement from the Editors of Anaesthesia on equity, diversity and inclusion. Anaesthesia 77 (2022), 1018–1022.
Zdravković, M., Nešković, V., Berger-Estilita, J., Surveys on gender issues among anaesthesiologists: where do we go from here?. J Gend Stud 30 (2021), 868–871.
Roberts, J.H., The feminisation of medicine. BMJ 330 (2005), s13–s15.
Flexman, A.M., Shillcutt, S.K., Davies, S., Lorello, G.R., Current status and solutions for gender equity in anaesthesia research. Anaesthesia 76:Suppl 4 (2021), 32–38.
Zhou, M., Abhishek, V., Derdenger, T., Kim, J., Srinivasan, K., Bias in generative AI. arXiv preprint arXiv:240302726, 2024.
Guilbeault, D., Delecourt, S., Hull, T., Desikan, B.S., Chu, M., Nadler, E., Online images amplify gender bias. Nature 626 (2024), 1049–1055.
Bianchi, F., Kalluri, P., Durmus, E., et al. Easily accessible text-to-image generation amplifies demographic stereotypes at large scale. Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, 2023, 1493–1504.
Stypińska, J., Franke, A., AI revolution in healthcare and medicine and the (re-)emergence of inequalities and disadvantages for ageing population. Front Sociol, 7, 2022, 1038854.
Marinucci, L., Mazzuca, C., Gangemi, A., Exposing implicit biases and stereotypes in human and artificial intelligence: state of the art and challenges with a focus on gender. AI Soc 38 (2023), 747–761.
Nicoletti, L., Bass, D., Humans are biased. Generative AI is even more. Available from: https://www.bloomberg.com/graphics/2023-generative-ai-bias/, 2024. (Accessed 4 April 2024)