Londa Schiebinger was the first speaker of the new lecture series Women Leaders that is organised and promoted by the Gender Equality Office.
She is the John L. Hinds Professor of History of Science at Stanford University and Director of the Gendered Innovations Project, which promotes gender analysis in science, medicine, engineering and the environment. A leading expert on gender in science and technology, she earned her Ph.D. from Harvard University in 1984. Schiebinger has directed key initiatives with the European Commission and U.S. National Science Foundation, and has published influential works on the history of women’s participation in science and gender.
You recommended a comprehensive approach to incorporating gender and diversity analysis in the design and funding of research. What is the status quo today ?
The main obstacle is that STEM researchers don’t know how to do sex, gender, and intersectional analysis—at the highest level of excellence—because it is not part of their professional training. The long-term solution is to include these methodologies in STEM curriculum. The short-term solution is to offer trainings to fill the gap. The Gendered Innovations website offers state-of-the art methods and their applications in the fields of the natural sciences, health & medicine, engineering, and environmental sciences. In the field of health and medicine, there are excellent online trainings offered by the Canadian Institutes of Health Research and the US NIH.
Luxembourg could take the lead to create online trainings in various fields of engineering or environmental sciences. The Luxembourg National Research Fund could require a certification of completion of the appropriate training as part of their application for research funding.
How can AI be used for gender inclusion in research?
AI is both an enabling tool to drive gender inclusion and a potential hurdle, especially through the perpetuation of stereotypes and non-diverse content.
The crucial step to ensure that AI helps, and does not hinder, gender inclusion is something called Embedded EthiCS in the US. This is a move to embed methods of social analysis in the core CS curriculum, meaning that students would not graduate without a basic understanding of how their work in computer science might impact society. This is the best way to ensure that AI will accelerate gender inclusion.
What are your recommendations to Uni.lu researchers?
Women academics have a very active Gender Equality Officer at Luxembourg University, Prof. Skerdilajda Zanaj. During the Mentoring Session, we learned, for example, that particular tables needed for specific physics experiments cannot accommodate a pregnant belly, so a pregnant researcher cannot continue with their work. It seems rather obvious that these tables could be redesigned to be adjustable to accommodate any researcher’s needs. Prof. Zanaj also did a survey to learn about women scientists’ needs and would be happy to do whatever her office can do to bring about structural change at the University so that everyone’s careers can flourish.