Internationaler Tag der Frauen und Mädchen in der Wissenschaft
Am 11. Februar wird die bedeutende Rolle von Frauen und Mädchen in den Bereichen Wissenschaft und Technik gefeiert.
FLRS setzt sich mit seinen Partnern dafür ein, Forschungsergebnisse in die Praxis umzusetzen und evidenzbasiertes Wissen anzuwenden, damit jedes Kind durch die Vorteile von Muttermilch einen optimalen Start ins Leben erhält. Hierbei verlassen wir uns auf die Beiträge zahlreicher herausragender Frauen in den Bereichen Wissenschaft und Technik. Heute freuen wir uns, Ihnen zwei dieser Frauen von der Universität Zürich vorstellen zu dürfen: Solveig Helland und Jing Fu.
Die beiden Forscherinnen setzen künstliche Intelligenz (KI) ein, um rund 1’400 veröffentlichte Stillmassnahmen auf ihre praktische Durchführbarkeit hin zu analysieren. Ihre Erkenntnisse werden in ein FLRS-finanziertes Forschungsprojekt an der University of Ghana zur Entwicklung von Leitlinien für die operative Bewertung von Stillförderungsmassnahmen einfliessen.
Lesen Sie das Interview, das wir mit Solveig Helland und Jing Fu über ihre Arbeit geführt haben (in Englisch).
Solveig, what are you discovering from your analysis of breastfeeding interventions – how does AI open possibilities or revolutionize the workflow?
As academics with limited knowledge of breastfeeding, we have been struck by its importance. We saw the substantial investments of time and resources dedicated to elevating breastfeeding rates. However, despite these efforts, there is a noticeable stagnation or failure to increase them. This discrepancy highlights a clear gap in understanding, or an unidentified element that is missing in the efforts to promote breastfeeding.
We very quickly realized that analyzing published breastfeeding interventions is very hard – because of the lack of standardization and the unstructured nature of the data – and until now there hasn’t been a good way except from having humans read each one and conduct manual annotation and analysis. We thought that with large language models, we could automate this process of structuring the enormity and trying to analyze it and see if we could start to see some pattern, similarities and differences.
It worked out really well. It’s a good proof of concept and it shows the potential is not specific to this application. It’s just showing off large language models and what they can do, and how easily it can be done.
Jing, where do you think AI is going in public health, and for advancing child and maternal health?
You can use AI technology or data science technology to optimize hospital services, like the waiting time in emergency rooms, or to help doctors recognize drug conflicts when writing prescriptions to prevent medical accidents.
I think there are a lot of things we cannot even imagine now, happening around the world to explore. It’s very interesting because data from hospitals is multimodal and it’s very hard to process. Now computer scientists are working on data processing to bridge the gap between researchers and doctors in the hospital. They can help the doctors to use information more efficiently in their practice. That’s very interesting.
AI assisted disease diagnosis is a very popular topic nowadays. Newborns and toddlers cannot really speak for themselves, so it could be very important to have health trackers to predict when your children might be suffering from a disease. Or based on the mamamap app showing where you can breastfeed on site in Switzerland, I think we can do a lot with that – not only promoting breastfeeding, but helping families plan proper nutrition for toddlers.
Solveig, you’re an AI Advocate at SwissCognitive, AI Network for Peace – what does this involve?
SwissCognitive is an amazing organization where they focus on putting all sorts of people from different organizations together around AI. They managed to make an atmosphere of sharing and helping – people talk about what is going on, the problems they’re having. It’s about discussing the technology, of course, but also, it’s a lot around ethics, a lot around regulation. The technology is amazing but it’s very hard to use in real life.
When I decided to study computer science, I never thought I would have these philosophical discussions. We always say we want fair algorithms, we want bias-free algorithms – but then, what is fair? What is bias? We don’t have good answers.
This is where the global and diversity aspects come in. If you only have one perspective, then you might not think about all these aspects around ethics or around fair technology, but once you have multiple voices in the room, it becomes very clear very quickly that different people value different things. Things that work in the West might not work in Asia. We can discuss how do we de-bias data or how do we de-bias algorithms. But that doesn’t work if we don’t know what de-biasing is in the first place. So having a platform to discuss these tough questions, these fundamental human values, is very nice.
What attracted you to STEM (Science, Technology, Engineering, Mathematics)?
Jing: I was born and raised in a relatively conservative, small city in China. In school, the teachers always said, ‘the scientific field belongs to boys and men, and women are not good at doing that’. I was angry about that. I was like, “I'm going to do that, even though I’m not the best student in science”. I wanted to prove that actually, women and girls can do that. I’m so glad – I found that you don’t need to be the best to go into any field, you can just be interested.
This field is so diverse, you can find anything you want here. If you are interested in art like I am, you can do visualization or computer graphics. If you are interested in music, you can also do music technology related to generative AI. Or psychology in the human-computer interaction. It’s like a jackpot!
Solveig: I was famously terrible at math, and I didn’t have any physics or these kinds of subjects at school. When I moved to Switzerland, I ended up studying economics, and just through the people that were around me, studying computer science, I saw what they were doing, and I thought it looked much cooler than economics. So I switched studies to Wirtschaftsinformatics, which is economics and informatics. I just dipped my toe into a little bit, and I absolutely loved it. So then I went full on-nerd and went into AI in my master’s.
What’s attractive for me is that I see it as something quite creative. You really get to make stuff. You have problems, you solve them – you make a solution. And then the really cool thing is that the solution can scale. You can have a big impact with just what you do at home.
Are there words of wisdom you would share with a young woman interested in STEM?
Jing: If you are interested in science, don’t listen to the negative voices. Don’t let others define who you are. You should be the one you really want to be. Explore and find what you want.
Solveig: Thinking about all these technical things and thinking “oh, I have to be so good at coding”, it’s not really true. At the end of the day, being able to code or being able to do these technical things, they are just tools that you can use to solve problems or to create things. For me, seeing it in that way makes it much less stressful – I’m just learning how to use tools.
You see this stereotypical person in STEM, and you might not see yourself. But there are so many different organizations now, like women in tech or women in data science, where you can see people who are like you and people who came from similar backgrounds. And suddenly this image that you might have in your head switches, and you see you can be exactly how you are.
Jing Fu is a Data Science master’s degree student at the University of Zurich.
Solveig Helland is an AI master’s degree student at the University of Zurich and a senior full stack engineer at Witty Works.
The International Day of Women and Girls in Science was established in 2015 by the United Nations General Assembly as “an opportunity to promote full and equal access to and participation in science”.
In 2024, UNESCO is calling on member states and all stakeholders to Close the Gender Gap in Science. They report that globally, just one in every three researchers is a woman. During a 9 February Symposium, they unveiled recommendations for closing the gap. Find them here.