How did your journey in AI start - what brought you to AI?
My AI journey started with my master’s thesis. I took a master’s in physics and wanted to do my thesis within medical physics. I started working under the supervision of a very inspirational professor at my institute, Professor Cecilia Futsæther. She was currently working on using AI withing cancer research and encouraged me to do a project using machine learning to aid in cancer treatment. After I started developing AI, I saw the incredible potential it had even for complex systems such as the human body. I wanted to learn more about this technology!
What is the biggest change you have observed in AI compared to when you entered the field at the start of your career?
The biggest change within AI is most probably the united focus on that AI methods should not only concentrate on accuracy and performance evaluation metrics, but that AI should also provide information in a way that we humans among ourselves share information. We don’t want black-box algorithms anymore, and as an example, we focus on creating Explainable AI – which is providing arguments and information about why a prediction or decision was made by an AI system. In addition, we are interested in learning what an AI system actually knows or does not know: We are developing methods to understand the uncertainty of AI systems. And as this focus increases, the development of methodology within these areas increases as well, and the newer methods are quite effective.
What excites you about working in AI, and what are you working on right now?
I have always been a data-driven person, who needs data and facts to support my believes. I found the first line and quote in my statistics book quite amusing and capturing this essence: “In God we trust, all other bring data” – William Edwards Deming. And this is exactly what AI does: It uses data to understand the characteristics of a system and uses past data to give insight into the future. AI is basically a very efficient way of drawing experience and information from data and making this knowledge available for us!
Right now, I am working on combining stochastic differential equations with neural networks, to both quantify and handle the uncertainties of an AI system. This means the parts of a system that are not possible to model by using the available data. By adding information about uncertainty into an algorithm, an AI system do not only tell us it’s prediction, but it can tell us how certain the prediction is, and the reasons for its uncertainty. It is basically the same as a human saying “I believe this is the truth, but I have limited knowledge about the situation, so maybe don’t trust me on this”.
How do you see the number of female researchers and professionals in AI grow? How do you involve yourself in initiatives to introduce more women to AI, and what initiatives can we make to inspire more women to pursue careers within AI?
I am happy to see that the interest among female researcher and professionals in AI seems to be growing! Unfortunately, we are still far from equal to the number of men at this area, like the tech industry in general. Also in research, women are heavily outnumbered. To close the gender gap within the field of technology, I think it is important to show woman and girls that a career within technology is as natural for us as any other career, and I think female role models play an important role in this. I try to make an effort of being a visible female AI researcher at different areas such as conferences, meetups, committees, and also just among colleagues. As an example, I am part of the board members of Oslo.AI, an organization promoting networking and sharing of AI experience.
In addition to role models, I think it is important to start getting girls interested in science, technology and math at an early age. This means that we need to engage families, schools and communities to promote STEM career to girls, such that this is a natural choice later in life! And it can also help with initiatives that tries to increase the self-esteem of youth working with science courses, such as free mentor programs and homework assistance. Luckily, we already have several initiatives as these in Norway already!
Why is it important for more women to get involved in AI?
Diversity is what brings forward both innovations, but also the creation of the ethical future we want to be a part of. When women are underrepresented in the development of technology and AI, this has consequences for the end products. Especially the use of AI sheds lights on gender and other kind of biases we have in our society, as it grows on the biased data we create. We have already seen commercialized AI products that are more tailored to men than women, as the algorithms were trained mainly on male data. We have also seen the same with e.g., skin color. Diversity in product development is very important, to make sure everyone’s interest and needs are taken care of.
What advice would you like to give women who are pursuing their careers in AI?
If I am going to give some advice to women interested in a career in AI, it would be: Go for it!
I have been working for AI for five years now, and it just gets more exiting. And with AI you can also pursue several interests! AI is always combined with other fields and areas, and you can choose which other interests you want to follow. Either if it is medicine, energy, biology, economics, AI just provides you with the tools which is needed to be a part of the future changes of other fields.
What advice do you have for organizations who wish to recruit and retain female researchers and professionals in AI?
I have a few points I would like to promote:
- Diversity if highly beneficial for every organization. Remember to promote the wish of diversity as a benefit to the organization, and not only something that has to be done.
- Make sure to have a respectful, collegial working atmosphere, which is inclusive for all the minorities of your organization.
- Increase visibility of female leaders and role models, and maybe consider mentor programs for your female professionals.
- Ensure transparency of statistics on salaries, bonuses, hiring and promotion, such that fairness can be monitored. Encourage policies to help reduce the gendered salary disparities. Make sure women are represented in recruitment committees etc. and potentially provide training against unconscious bias.
- Last but not least: Create family friendly environments, which makes it possible to have a work-life balance without destress. This is often important to keep women employees in the long run.
Who is your biggest role model within your field, and why?
I am lucky to have several inspiring women around me within the research world. If I have to
mention some, I would like to mention Professor Cecilia Futsæther, Professor Ingrid Kristine Glad, Professor Giulia de Nunno, as well as Associate Professor Kristina Rognlien Dahl: They are all very successful researchers in my field, are good at being visible and encouraging younger professionals and students. Huge thanks to all of you for being role models!
What role can NORA take on to empower diversity and inclusion in AI in Norway?
I think NORA is already doing an excellent job of promoting female AI professionals and researchers. However, as NORA has become a link between universities, the research world of AI as well as industry, it is important to continue to focus on diversity, by making sure women are represented in all your aspects: e.g., committees, meetings, as speakers, representatives and leaders. Also make sure to have diversity on the agenda when planning and transferring knowledge between different parties!
About Alise
Academic interests
- Machine learning and Data Science
- Explainable Artificial Intelligence
- Applications within risk and safety
- Physics-informed machine learning
Background
Alise did her Master of Science at the Norwegian University of Life Sciences (NMBU) in Environmental Physics. In her thesis, she combined machine learning with image analysis of PET/CT-images of cancer tumors, to predict the treatment outcome. After that, Alise worked with technology consulting for Accenture, working with new technologies like machine learning and extended reality. Since August 2019, she has been a PhD Student at the Department of Mathematics at the University of Oslo. Here she is working on modelling and analysis of multidimensional high-resolution environmental data within safety-critical systems such as airport runway condition management.
Publications and Preprints