Paula Gordaliza (Valladolid, 29 years old) uses mathematics to try to make society a little fairer. The young researcher, winner of the Vicent Caselles prize awarded annually by the Royal Spanish Mathematical Society (RSME) and the BBVA Foundation, has developed a system to correct the bias of artificial intelligence (AI) algorithms, capable of making more accurate predictions than an expert person. “The problem is that these decisions are not always socially responsible,” explains Gordaliza in a video call. Researcher at the Basque Center for Applied Mathematics in Bilbao and associate professor at the Public University of Navarra, she began to study a way to eliminate algorithm biases during her PhD at the University of Toulouse under international joint supervision with the University of Valladolid, when AI was not yet under the scrutiny of regulators and public opinion. “Things have evolved very quickly over the last five years. Now more than ever, it is important to work on the effects that artificial intelligence has on people’s lives”, says the researcher.
Ask. Do you use artificial intelligence a lot in your work?
Answer. I like to remember that I, first of all, am a mathematician, and what I do is research in mathematics. My work consists of the foundation of the theoretical bases that are needed to develop any technology, in particular artificial intelligence. So I deal more with studying mathematical problems and how, once these issues are resolved from a theoretical point of view, they can be applied to real problems. In my case, we are talking about machine learning and algorithmic fairness, which are included in the field of artificial intelligence.
Q. How are mathematics and artificial intelligence related?
R. Mathematics is behind all scientific and technological advances, and in recent years AI is the most fashionable form of advancement. What mathematics does is establish the theoretical bases to solve the problems we face, which in the case of my research would be the algorithmic biases of artificial intelligence.
Q. What is an algorithmic bias?
R. It is somewhat complex to explain, because they are words that have been used so much, they have been given many meanings depending on the context. In statistics, something biased is something that does not behave as expected. While, if we go to the field of artificial intelligence, where this word is used a lot, it refers more to inclinations or prejudices in favor of or against a group or an individual based on certain characteristics, such as gender or skin color. Perhaps it is this that contributes to the fact that algorithms cause fear and mistrust in people.
Q. For what is this?
R. We are witnessing widespread use of artificial intelligence systems, algorithms in particular, and this is being seen in aspects that directly affect people’s lives. The granting of credits, in the selection of personnel for a job or in the clinical field, to decide who to apply a treatment to or make a diagnosis. There are many more examples, but these are perhaps the most common. And of course, the fact that algorithms can decide on these issues generates fear and restlessness in the population. This will happen until they receive guarantees that these algorithms are fair, that they are reliable, and that they are interpretable.
Q. What can be done to make this fear disappear?
R. This is where the importance of mathematics can be seen, as it helps us to understand how algorithms are working and is the tool to open the black box of artificial intelligence. It is important that the message gets across that algorithms do not work alone, that whoever uses them knows what they are doing and why these decisions are being made. This would go a long way in reducing the mistrust people have of you.
R. You have spoken of prejudice and discrimination. Are algorithms racist?
Q. Algorithms are not racist or sexist. Algorithms learn from data. Machine learning is a form of artificial intelligence that is capable of making predictions and establishing connections from huge databases, which it is capable of managing at high speed. The problem comes when these data are not of quality. For this reason, it is essential to bet on having quality databases that are not biased with respect to variables that may contain sensitive information, such as race, gender, disabilities, sexual orientation or any other information that may be susceptible to discrimination.
Q. This is what your research is about.
R. The idea was to try to create two population subgroups, for example, men and women, that were as similar as possible in the rest of the characteristics. In this way, I tried to delete the gender information so that the algorithms are not able to learn about the gender of the people and keep the information provided by the rest of the database.
Q. Society has improved when it comes to discrimination. Why do so many biases still exist?
R. It is not a problem of society. In the end, a tool is being used that learns from historical data and those are biased, this is how the algorithm learns it. What should be done to move forward is to encourage research, because it is a matter of knowledge and the frontier of knowledge is increasingly complex. If we want to improve, we need multidisciplinary teams made up of mathematicians, statisticians, computer scientists and more professionals who contribute their part to the cause. All points of view are needed, not only the mathematical perspective, which also needs to be greatly improved.
R. Surely, an academic and research career should be promoted, in order to have quality research focused exclusively on artificial intelligence, but with very solid mathematical bases that ensure that what is being done with the algorithms is reliable, safe and fair. To achieve this, there needs to be a motivation for young people to be attracted to this career, which in these times is quite difficult. It is important to improve the conditions of this profession, especially in the early stages. Before, at the age of 30, you were already a tenured professor, while now, at 29, I am still going to start as an assistant.
Q. When you talk about motivation, are you referring to financial resources?
R. In part, but there are also other factors to take into account. For example, feeling that you are advancing in your career and that you are getting more and more relevant positions. Feeling valued is essential to stay in Spain and keep trying.
Q. You have done your doctorate in France. Do you think there are more possibilities abroad?
R. There are many possibilities, but also in Spain. The mathematics that is done here and the research that is there is of high quality. I have already gone through this experience of living abroad and I am sure that throughout my career I will also have other opportunities to do international studies, which is undoubtedly something that gives great value to your career and provides a lot of projection. But my ultimate goal is to stay in Spain, where research, at least in my field, is progressing a lot, and the importance that artificial intelligence is gaining is going to give us a lot to work on.
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