ACM ByteCast - Nuria Oliver - Episode 29

Episode Date: September 20, 2022

In this episode of ACM ByteCast, Rashmi Mohan hosts Nuria Oliver, Chief Scientific Adviser in Data Science at the Vodafone Institute, Chief Data Scientist at Data-Pop Alliance, Scientific Director and Co-Founder of ELLIS (the European Laboratory for Learning and Intelligent Systems), and Director of the ELLIS Alicante Foundation (the Institute of Humanity-centric AI). Recently, she co-led the winning team of the XPRIZE Pandemic Response Challenge, ValenciaIA4COVID. She has more than 25 year of research experience in AI, HCI, and Mobile Computing. Oliver is the first woman computer scientist in Spain to be named both an ACM Distinguished Scientist and an ACM Fellow. Her research has contributed to the development of intelligent multimodal interfaces, context-aware mobile computing applications, personalized services, and Big Data for Social Good. She holds more than 40 patents and many awards, including the King James I Award in New Technologies and the Abie Technology Leadership Award from AnitaB.org. Nuria, who was always fascinated by the idea of investigating and solving unsolved problems, shares how she fell in love with AI while studying telecommunications engineering and highlights some of her earlier work on smart cars, smart rooms, and smart clothes. She talks about her recent work helping the government in Valencia, Spain to develop evidence-based policies using data science that were instrumental during the COVID-19 Pandemic, as well as the Data-Pop Alliance, an initiative created by the Harvard Humanitarian Initiative, MIT Media Lab, and the Overseas Development Institute to use data for social good. Nuria also stresses the importance of inspiring girls to pursue computer science and her own efforts in advocating for diversity in the field.

This is ACM ByteCast, a podcast series from the Association for Computing Machinery, the world's largest education and scientific computing society. We talk to researchers, practitioners, and innovators who are at the intersection of computing research and practice. They share their experiences, the lessons they've learned, and their visions for the future of computing. I am your host, Rashmi Mohan. The world of artificial intelligence and machine learning is having its moment in the sun.

Every organization worth its salt is finding avenues to weave the magic of AI into their way of work and grow their business. And yet, what good is artificial intelligence if it doesn't benefit the larger population? Our next guest has been answering that very question for the last 25 years. Nuria Oliver is the first Chief Scientist Advisor in Data Science at Vodafone, the Chief Data Scientist at Datapop Alliance, and the co-founder of ELIS, the European Laboratory for Learning and Intelligence Systems. Her work involves building computational models of human behavior, studying human-computer interactions, and using data for social good. She has over 40

patents, a slew of awards, with the most recent ones being the Abbey Technology Leadership Award and the King James I Award for In-New Technologies. She led the team Valencia IA for COVID-19 that won the most recent XPRIZE Pandemic Response Challenge. She has the honor of being the first woman computer scientist from Spain to be named an ACM Fellow and with over 21,000 citations, is one of the most prolific female computer scientists in the country. Nuria, welcome to HCM Bytecast. Thank you. Pleasure to be here. Entirely our honor as well. I'd love to lead with a question that I ask all my guests, Nuria. If you could please introduce yourself and talk about what you currently do

and give us some insight into what led you into this field of work. Yeah, so I'm Nuria Oliver. I'm a computer scientist. I'm an expert on artificial intelligence. And I actually wear a lot non-profit foundation that I've created called the Institute of Humanity-Centric AI, which is the only ELIS unit that there is in Spain. ELIS means the European Laboratory for Learning and Intelligent Systems, and is European Association of Scientific Excellence in Artificial Intelligence. I also advise a lot of governments, universities, foundations, research centers, companies. I'm also involved with Data Pop Alliance, which is an NGO devoted to the use of data and AI for social good. And I invest a fair amount of time in outreach activities

to communicate both to technical and non-technical audiences. So like findings in my research area, which is human-centric artificial intelligence. In terms of what brought me to computing, since I was very, very small, I've always been fascinated by the idea of an inventor or a scientist. I love mysteries. I love unsolved problems. I love questions that no one knows the answer for or inventions that no one thought about. So I was fascinated by figures like Leonardo da Vinci or Marie Curie or Albert Einstein. But of course, all these prominent scientists were dead when I was growing up. So I couldn't really ask them, you know,

how they got where they were and what they did. So I wasn't sure what to study. I chose the scientific technology track in high school. And when I was in my last year of high school and I had to figure out what to study in university, I had a chance to talk to one of my brother's best friends who had started telecommunications engineering in Madrid, in Spain. I met with him and he told me what engineering was about. He told me what technology was for and what kind of classes he had. And I basically came back from that meeting very inspired, convinced that telecommunications engineering was what I wanted to study.

So that's what I did. I went to Madrid and I did a six-year program. Telecommunications engineering is like a bachelor's and master's program together. And maybe since third year, I fell in love with artificial intelligence. I discovered what artificial intelligence was and I decided that that's what I wanted to do for the rest of my life, I guess. That's really inspiring. I mean, I think what you bring about

is so critical as well, right? I mean, as young students, we're a lot of times we're confused about what we want to do and how do we discover these new areas and having those mentors are so critical for us to make those decisions. And like you said, it's also that process of discovery and, you know, sort of you get into a field, but as you go deeper, there's like one class or one teacher that really sort of

inspires you and then you sort of pursue that path. So it's really fascinating to hear about your journey in that manner. But your work around HCI and intelligent user interfaces, Nuria, has been prolific since then. Would you care to talk about some of the highlights that you feel across your career? I know a lot of your work now is in the non-profit area, but in terms of your research, some highlights from your journey that you would love to share with our audience? Yeah, definitely. So my sort of like vague idea or my aspiration since I started studying engineering was to invent technology that would help people,

to invent technology that would somehow help us have a better quality of life or would help us tackle the problems, the big problems, the big challenges that we face. So I started working on computer vision, which is one field within artificial intelligence to make computers understand videos and images. And for my master's thesis, I did a project on detecting cars in highways back in 1994 to have better, safer driving and more sort of like accurate models of traffic and so forth. Then I went to do my PhD at MIT and I continued working on what is called perceptual computing, which is not only computer vision, but also analyzing data coming from

other kinds of sensors to help computers have an understanding of what is happening around them. So I worked on building smart rooms. For example, I did one of the first systems in the world to do facial expression recognition in real time so the computers could understand our emotions, for example. I also worked on a gesture recognition system so that so we could interact with the computer you know using our own gestures and i built a smart car a car that was able to predict the next maneuver that drivers would do so we could have safer driver driving and i also had the opportunity to work on smart clothes,

which was a concept that was being defined at the time back in like 1996. Basically, smart clothes are clothes that have technology embedded in them and do something useful for people, the people that wear them. So in 1996, we organized the first smart clothes fashion show in the world at the Media Lab. And I collaborated with some design schools in the world, some of the best design schools. And the designs that I worked on were a female

and a male version of a system to help people who had a hearing disability and also they were mute, they couldn't speak, communicate with other people. So the design had a little camera that was pointing at the hands of the person. So when the person was talking using American sign language, the camera would capture the hands and the outfits had a backpack where there was a computer. So the computer would recognize using artificial intelligence, the signs that people were signing and would interpret what they were trying to say. And then using voice, sort of like a text to speech synthesizer, the clothes would talk for the person. So there were some speakers also embedded in the designs. And then, you know, you would do your gestures and then the clothes would talk for the person. So there were some speakers also embedded in the designs,

and then you would do your gestures, and then the clothes would talk for you. So it would be a system to help people communicate with people who didn't know American Sign Language. When I finished my PhD, I moved to Microsoft Research, and I continued working on this concept of building smart anything. So I built smart rooms, a number of smart offices and rooms, an office that would know what you were doing. So it could help you avoid interruptions or it would help other people know better when to call you or when to reach out to you. I also built with a colleague a system to manipulate the windows on your computer just using your gestures, something that today has become sort of like commonplace, but back in the year 2000 or 2001 was really very novel. And in 2005, I realized that if I wanted to build technology that understood us and that helped us, probably the most personal computer, even back at the time, was the mobile phone. So I decided to shift my

attention almost exclusively to the mobile phone. And I built some of the early works on sort of like using the phone as a computer, not as a phone. So I built a system to detect sleep apnea. I built another system to help people achieve their exercise goals using persuasive computing. In 2007, they offered me the opportunity to come back to Spain. I'm originally from Spain to create and lead a research team and the entire research area of data science and AI within a very large telco in Spain, Telefonica. So we took on that challenge. We moved back to Spain from the U.S. And since then, joining a big telecommunications company opened up an entire world of opportunity

in terms of analyzing large-scale human behavioral data captured by the mobile network infrastructure and using that data for social good. So one of the areas that I worked on since 2008 is how we can use this large-scale human behavioral data for social good, for example, to help us better respond to natural disasters because we can understand how many people have been affected by the disaster and where they are, to help us foster financial inclusion, because we can automatically infer the socioeconomic status of a region, for example,

to help us have safer cities. We did a system to predict crime hotspots, for example, in cities, and also to help us better respond to pandemics and infectious diseases. So these are some of the, I guess, topics that I've worked on over the years. And as I mentioned earlier, since for the past few months, I have been the director of this new institute. And the main focus of the institute is to do scientific research on artificial intelligence for social good. I think that today, more than ever, we really need to invest in intellectually free research on understanding the societal implications of artificial intelligence and on inventing and developing artificial intelligence algorithms

and methods and systems that actually have people's well-being at the core and as the main goal for the systems, as opposed to having other interests like maximizing the amount of time that we spend using the systems or maximizing the amount of money that companies make as a result of that. Yeah, that's amazing. Thank you for sharing that because, you know, that's one of the things that I also gathered as I was, you know, studying your work in preparation for this conversation was the fact that you've been harnessing the power of mobile data for so many years and the applications that you've been working on have such relevance in our lives even today. I mean, and you were obviously working on these many, many years ago, but I'd love to tap into this idea that you were talking about, which is really, I mean, this is something that has, I think, guided your career from what I can tell, which is the using of data science for social good. It almost as if you kind of had a crystal ball when you were, I know you

were talking, I mean, you know, many of your very, very early interviews, we're looking at the impact of people moving and the spread of the pandemic. I know you were looking at the H1N1 outbreak, you know, way back when, and you know, the value that you may have gotten from those studies and how it sort of, I was wondering if you could talk about like, how has it helped you sort of respond to, you know, the COVID pandemic and sort of help guide the governments or the organizations that you work with? Yeah. So there is a world movement that I belong to on leveraging this large scale data, you know, that there is to support better public policies and better decisions, decisions that impact the lives of millions of people. The idea is to move into

what is called evidence-driven policymaking or even evidence-driven decision-making. So as opposed to coming up with policies that are based on intuitions or obsolete knowledge or political interests to transition to a situation where those policies are actually informed by evidence and by scientific sort of like results. So in this context, and having worked on the use of data analyzed using machine learning methods in the context of pandemics since 2009, 2010, where my team at Telefonica, we did a project on the H1N1 flu outbreak. And then with my team at Vodafone, I did a project on the Ebola outbreak that took place in DRC. And at the time when, back in February of 2020, when the COVID-19 was starting to exist and spread, actually, I was working with my former team at Vodafone on a project on modeling the spread of malaria in Mozambique,

leveraging large-scale mobile data. As I saw that the pandemic was going to happen, I really felt compelled to reach out to the government in Spain and the government in the Valencian region of Spain, which is sort of like this. So the equivalent would be the state, you know what I mean? Spain has a federal model like the U.S. And we have 17 autonomous regions, which would be like equivalent to the states in the U.S. To reach out to them and offer them this idea of creating a team of scientists and experts working very closely with the policymakers so they could leverage all this knowledge and all these methods that we have today to support their decision making.

So my proposal was very well received by the presidency of the Valencian government in Spain. And immediately they said, yes, we think this is a great idea. Let's create data science for COVID-19 team that you will be leading. So they reached out to all the scientists in the region, in all the universities and research centers that had any kind of like background that was relevant. They organized a meeting. I explained my vision for what we could

do and the different areas that we would work on, from modeling large-scale human mobility to building computational epidemiological models, building predictive models of hospital occupancy and intensive care occupancy, also to infer the prevalence of the disease, because at the time there were no tests, and also reaching out to people in a very large-scale citizen survey that we launched in March of 2020 and is still active called COVID-19 Impact Survey. So based on my description and I guess my enthusiasm, more than 20 scientists said that they wanted to join the team. And we created this virtual team. This was March of 2020.

You know, we were in lockdown at the time. It was the very beginning of the pandemic. So I organized meetings every day with a team that I had never seen in person and that I had actually never worked with before. And I didn't have a chance to actually meet them in person until over a year later. We saw each other every day and we worked really well together, very intensely, you know, for many, many, many months. So I wrote reports every day with predictions of the day,

the number of cases, number of hospitalizations, number of deceased, number of intensive care unit occupancies, reports on relevant topics, reports on summarizing the results of the survey. And I felt that, you know, we were really listened to and that our results and our recommendations were really considered. And I think one of the key elements for the success of our team is that a policymaker, the Director General for Public Policymaking, is actually a member of our team, even though she's a politician and she works for the president of the region. So having this

multi-institutional, multi-disciplinary team where the policymakers are committed active members of the team, I think is absolutely necessary. She came to every single meeting every day. She made a huge effort in understanding the results of our work, understanding what we were doing, helping us prioritize our work, providing questions for us to answer, and translating all these technical results into actionable insights that they could use to support their policymaking. And yeah, so that's the experience that we've had in the Valencian region of Spain. Because of the uniqueness of this initiative, we've received some international recognition. So we've been featured in different international media like Wired or Politico or MSNBC. And we've also got some internationals

of like validation of our work when we participated in the XPRIZE Pandemic Response Challenge competition. And we actually won it, which was really amazing. Truly, truly amazing. Congratulations for that. There's so many things in your answer that struck me as so amazing. I mean, one thing is just the fact that you had the vision to actually reach out and be proactive about the help that you could provide to the government. I mean, that's a significant sort of, I would say, a factor in the success of this entire project. The fact that the government was progressive enough to be able to recognize the value that you could bring and the commitment, right? Like you said, bringing that policymaker in and being

an integral part of that team shows a level of commitment that you're going to use this work and use it in a meaningful way, which is very encouraging, I'm sure to the entire team. But what is amazing to me, Nuria, is also when, you know, oftentimes when we're working on a really critical project, you know, especially as a leader of that project, you like to assemble your team because you kind of have a vision of like, hey, these are the skills that I need in order for me to sort of take this vision into reality. In your case, it almost felt like somebody else assembled that team for you. How did that work? And how did you make that sort of be successful in the initiative that you were

driving? Yeah, so that's a good question. I mean, everyone volunteered. So there were a lot more people in this original meeting. And out of these, I don't know, maybe there were 40 people or something. So 20 plus, I don't know, 20, I don't remember the exact number, 22, 25 decided to volunteer. I had a vision for the different work streams that we were going to have and the different sort of like expertises that we were going to need. So we tried to find the best match between the different people that wanted to help and wanted to volunteer and then what we needed, you know, and what we wanted to need. So we try to find the best match between the different people that wanted to help and wanted to volunteer and then what we needed, you know, and what we wanted to do. To me, as much as scientifically, also societally, we have had impact, I have to say that at a personal level,

it has been an extremely fulfilling and reaching, you know, unique experience. Because I've really thoroughly enjoyed working with this team. If there is something that we are proud of is that we've worked really, really well together. We've never had a single conflict or argument or fight among anyone. And it's a pretty large team. And I think one of the reasons is because we were all joined and united for a common purpose. And that purpose was really drove us, you know, to be working for so many, many months. Well, I mean, for two years now on that particular

topic. And we worked, you know, day and night and weekends and holidays, you know, no one was asking anyone to do it, but we all had this really strong drive to try to help. And we felt holidays, you know, no one was asking anyone to do it. But we all had this really strong drive to try to help. And we felt that, you know, we could help. Yeah. So I don't know how that happened. I think partly it's because of this common purpose that really brought a lot of meaning to everyone. And I think also partly it's because it was a refreshing experience in the sense that we didn't have any hierarchy. We didn't have any bureaucracy. Everyone could help. You didn't have to ask permission to anyone.

There was no boss, really. So we had both from undergraduate students to full professors. We had the whole range of seniority. So it was a very diverse team. And it was really, I mean, it was really a flat structure. It didn't matter who you were. Everyone wanted to join. Everyone wanted to help. We allocated tasks that needed to be done and people took on responsibilities that they were accountable for. And, you know, because we met every day, we could actually be very dynamic in responding to new needs or new analysis. But also, no one had to ask permission to anyone. No one had to fill out any forms. No one had to apply for any grant. We were just really hands-on. Let's do this because we can help. And we feel there is nothing more important to do, you know, right now. So I think everyone felt there was a great way of working compared to the, I guess, normal way, which particularly in Europe,

in academia, is very bureaucratic, and it's very hierarchical. So it's very rare to have an opportunity where you can just like, be free to work on whatever you want. Usually, you need to ask for funding, you need to apply for grants. You have to teach. You have to all this like hierarchy. If you are a postdoc or if you are a student, you really don't have a lot of autonomy. And I think the fact that this team

was the opposite to that, was very motivating to people. And the same happened with the XPRIZE competition. We had from students to professors all working together. And I think it was very inspiring to everyone, including to me, because I realized that, you know, there is talent everywhere. And many times it's not a lack of talent, it's a lack of an environment that enables that talent to flourish and to grow and to realize its potential. And many times the environment doesn't allow that. It's too restrictive or it's too bureaucratic or it's too hierarchical and it demotivates people. So for me, it was also inspiring because having lived outside of Spain for a long time, realizing, you know, that there's this amazing talent anywhere that we can not only have local impact,

but also win a world, you know, international competition was really a surprise even, you know, that a very positive surprise that I felt it was very inspiring because I realized, wow, anyone can do anything, you know, if they set themselves to it. Yeah, what an incredibly valuable lesson you bring up, Noria, which is really about when a team gets together with a common purpose, and that purpose is really in solving a problem for the greater good. It definitely inspires us and the fact that I think having this sort of flat structure empowers everybody to really participate in a way where they're bringing their best ideas you're not sort of intimidated you don't feel like there are any repercussions and you're really all all gunning

for the same goal so you know it's really inspiring to even hear about the way you describe it. Thank you. I would also love to know as a result of this work, how did you all measure the efficacy of your work, right? What were the sort of metrics you use to sort of constantly guide you and say, yes, we're moving in the right direction, or maybe, hey, we need to sort of change course? Well, I mean, we had daily feedback and constant feedback because we were having daily meetings. I was writing daily reports with predictions. So we had this constant feedback on whether we were doing well or not and whether our analysis would be valuable or not. So it was fairly immediate. And that also gave us the opportunity to react,

to do new analysis if they were needed to adjust things. So we did have a very close interaction in a very short cycle, which was just daily, maximum daily, you know, sometimes, you know, more than once a day. So, yeah, I mean, I guess some of the, there were a few moments where maybe we felt proud of our work. One was after Christmas of 2020, 2021. So January 2021, right after Christmas, that was the third wave of infections. And that was the worst wave of infections in particularly in the Valencian region of Spain. Our model worked really,

really well. That was a model that we had developed for the XPRIZE. It was a very stressful moment where there was a lot of concern about a potential collapse of the healthcare system, because the number of cases was growing exponentially, the number of hospitalizations, the number of intensive care units occupied. And there was a lot of pressure and need to have good predictions to really prepare and to really order ventilators you know and to really free hospital rooms and so forth but they need to have somewhat accurate predictions you know to be able to prepare and our model worked really well I mean at some point I even warned the government well maybe you shouldn't put too much faith in this model because at the end of the day it's just a model that we built, you know, we haven't really,

I mean, no one knew with this pandemic, right? The virus was also mutating. And yeah, it worked very well. And we felt really happy and relieved that our predictions were so accurate, and we could really help. Something similar happened just recently with the sixth wave, with the Omicron wave, where our model also worked really, really well. And in both cases, it predicted very accurately the day of the peak of the infection and the number of cases at the peak of the infection. We could see how we helped. We also helped in changing the perception of the pandemic. So I remember back in beginning of April of 2020, the president wanted to give a speech to

tell citizens when there wouldn't be any more cases, you know, when somehow the pandemic would be over. And we told them that that was not possible to tell that the virus was going to continue to exist and the virus is going to continue to infect people. And moreover, that we hadn't reached herd immunity and that they with very high likelihood, there was going to be a second wave of infections. And he listened to us and he never said anything related to, oh, you know, we've defeated the virus or, you know, this is over, you know, when the first wave of infections finished. So I felt happy to have been able to bring maybe a little transformation that the public administrations and the governments need to undergo, but they hadn't undergone yet before the pandemic.

And I think our experience and seeing the value of being a data-driven organization and a digital organization has really inspired the government into transforming itself and even thinking of creating a data science unit within the government works is probably one of the, I guess, most impressive outcomes from my perspective. Because most large companies had already undergone many, many years ago this digital transformation, but many governments haven't. And I think we've paid the price with this pandemic in terms of the lack of data, the lack of like, it's been a complete chaos, you know, in many cases. And I think seeing the value through our project

has really inspired them into realizing that they need to become more digital and they need to become more data-driven. Yeah, no, absolutely. I'm sure that must be an incredibly rewarding sort of outcome simply because you demonstrated the value of actually sort of being digital first and to see that change happen as a part of the government, as a part of the region that you're in and the country is very inspiring and that's incredibly impactful work.

So amazing to hear about that. ACM ByteCast is available on Apple Podcasts, Google Podcasts, Podbean, Spotify, Stitcher, and TuneIn. If you're enjoying this episode, please subscribe and leave us a review on your favorite platform. Nuriya, I wanted to talk a little bit about one of the other initiatives that you work on. I mean, I was again, reading through your bio, I am amazed at the amount of varied interests that you have and the impact that you drive in so many different arenas. But one of the things that stood out was the DataPop Alliance and the work you do in that organization. I'm wondering if you could take us through that journey. How did you

hear about it? How did you get involved? What is it that you do for them? Yeah, so as I mentioned earlier, since 2008, I've been working on the topic of data science and AI for social good. And DataPop, I think maybe it was created in 2014, maybe. I can't remember exactly the date. It is an initiative created by the MIT Media Lab, the Harvard Humanitarian Initiative, the Overseas Development Institute, and Flowminder. So I did my PhD at the MIT Media Lab and the main professor behind Data Pop Alliance from MIT is Sandy Penland, who is my PhD advisor. So

I think I got probably to know about it through him and Emmanuel or Manu Le Touze, who is a co-founder and director of Data Pop Alliance. So I guess, you know, we started kind of collaborating. Initially, I was, I can't remember the title, some kind of like, I don't know, research affiliate or something like that. But since 2016, I think, I am the chief data scientist. And basically, I just help in whatever way I can in whatever project I can. and helping draft policies and writing sort of like thought leadership articles in the area of data, AI, and social good and development, to doing education and outreach and data literacy programs. And basically, I'm available to help in whatever way, depending on the project and depending on

the timing. Today, for example, I just had a meeting with Manu just a little bit earlier today. So it's sort of like a flexible arrangement where I help as much as I can in whatever capacity I can, depending on the project, yeah. I got it, yeah. Looking at the Alliance team itself, Nuria, it seemed to be an of geographically diverse team. And so is it that each member sort of brings the sets of challenges as well as the ideas that have

worked for them to the alliance and sort of helps in knowledge sharing? Is that a part of the work that you do? Also, I know that you've always been a huge proponent of diversity in all of the teams and all of the efforts that you've worked with. What is the greatest value that you've always been a huge proponent of diversity in all of the teams and all of the efforts that you've worked with. What is the greatest value that you see from like this cohort of people that you work with? Well, I mean, Data Pop is a very special initiative. It's extremely multidisciplinary and diverse. And as you can see on the website, I'm also very proud that in terms of like gender diversity is also, you know, very balanced to be an initiative that is about data and AI, you know, and social good.

And basically, depending on the project, depending on the geography, different members of the team work on the project. DataPop has a lot of different funding agencies from the Rockefeller Foundation to United Nations, different institutions in the United Nations, to the French Agency for Development, the Inter-American Development Bank. So there's many different foundations and organizations that fund the work that Data Pop Alliance does. And the scope of the work geographically is mostly developing economies in Latin America, in Sub-Saharan Africa, and also in some Asian countries. And as I mentioned, in terms of the nature of the work, we do both work in terms of using data to better understand issues, for example, inequality, discrimination, migrations, violence, crime, you know, and so forth.

We also do projects and actions in terms of education, a lot of like workshops and courses. And then we also do projects in terms of supporting policymakers and governments draft strategies or, you know, really impactful projects that would enable them to become more data centric, always with the goal of having positive societal impact. So when a project is defined, then a team is created and different people from Datapop join the project. And then sort of like the project is sort of like executed, you know, and carried out. Yeah. Great. I mean, that sounds like an incredible opportunity.

Is it mostly volunteer driven, Nuria? Like, you know, can anybody join or how does this work? No, there are employees from Dat DataPop and then there is also volunteers, so it could be both depending on the project. Yeah, probably if anyone is interested, the best is if they could just email

either the director, Emmanuel Le Touze, or someone. I mean, there's a lot of information on the website on also how to contact. The Institute of Humanity-Centric AI that I created, which is also a nonprofit, obviously we have a lot of collaborations also with DataPop Alliance.

But the focus on the Institute of Humanity-Centric AI is scientific research. So we do outreach activities as well, but it's less, I guess, less broad than DataPop, which covers a lot of, maybe not so much scientific research as more working directly with countries and organizations to have impact. And in the Institute of Humanity-Centric AI, we focus more on inventing new algorithms or carrying out research projects that reveal the impact that AI is having on our society and on our lives. And in many cases, they're not so positive impact that it's having. Got it. Yeah, no, and then I think it's important to actually be aware, right? And then I think

that's the only way to sort of make the impact. So I think sometimes the results may not always be the greatest that we want to hear of, but I think awareness is so crucial in order to be able to tackle those problems. I mean, exactly. I mean, if you don't know, it's very difficult, you're going to change it, right? So the first thing is to know. Yeah, right. Yeah. Yeah. And I want to go back to something that you said earlier around the team itself at Datapop and the both in terms of geography. I know I brought up geographic diversity, but you brought up gender diversity. I know that's something that you're very passionate about, Nuria, about really starting early in terms of inspiring and educating young girls into the world of technology, as well as data science and AI in particular. Would you like to talk a little bit more about that? About gender diversity, you mean? Yeah, just your sort of interest in the work that you've

done, your thoughts around like how we can improve and inspire young girls to be in data science. Yeah, so we have a big societal challenge here, particularly in Western Europe and North America, which is the progressive loss of female talent into computer science. This wasn't always like this. In fact, up to the mid 80s, the percentage of women in computer science was actually increasing. But then since the mid 80s has been decreasing. And right now there are a lot of degrees, for example,

in Spain that have less than 10% of girls. They are all within computer science. So I don't know, robotics or, I mean, different kind of like branches of computer science. This is obviously undesirable because we live in a technological world. We need technology to survive as a species. And however, this technology that we all use,

no matter who we are and where we are, has been designed by non-diverse teams. And we know that this has severe implications in terms of how innovative that technology is, in terms of how inclusive that technology is, and also economically in terms of how much money one could make, you know, with those results. Only in Europe, for example, the lack of gender diversity in the technology sector is attributed to cost in the sort of like billions of like euros. So that's something that has worried me for many years. Also, the difficulties because I have created research teams and it's been extremely difficult to find female scientists with a PhD in computer science that could join the teams. Even now, I'm trying to recruit a lot of scientists for the Institute of Humanity-Centric AI. It's very hard to find female PhDs in artificial intelligence. So if anyone is listening that could be interested,

please reach out to me. So I've done everything I could to try to inspire, in general, young people, because we don't have enough young people, but particularly girls and female adolescents, like teenagers, to pursue careers in computer science and in technology. I have also joined different initiatives that have as an objective to increase gender diversity in the tech sector. So I am a fellow, I'm a member of the Spanish Royal Academy of Engineering. The Spanish Royal Academy of Engineering has a project called Women in Engineering, and I am the fellow, I guess, director of the program. So the program has an executive director,

but who is not a member of the Royal Academy. And it has to have a director who is a member of the Royal Academy. So I'm that person. I'm also a member of an initiative called Women at the Table that aims to have more women at the table, I guess, at the decision table.

And then it has an initiative on algorithmic discrimination, like gender discrimination on algorithms and also a member of that. So I've actually helped create and organize very large conferences for students so they can learn about technology and they can see role models that are female and that are not the stereotypes that the tv you know series or the movies or the books show to us so they realize that computing is also for girls and it's a well it's the best profession that you could have. So yeah, so I think the main message is we have really a societal challenge here that we need to tackle because we should not, as a society,

accept that the technology that we all use hasn't been designed by diverse teams. We should not accept that, I guess, the richest sector right now in the world doesn't have a good representation of women. And we are failing as a society to inspire the next generation of girls and boys, but particularly girls,

to pursue careers in a field and a sector that has the lowest levels of unemployment, and the largest levels of opportunities. We do need to act. If we don't do anything, the situation is not going to get resolved by itself. And that's why we need to implement many, many actions from transformations in the education system

to creation of role models, to giving greater visibility and recognition to women, removing the pay gap, to supporting the women that are in the field. As you probably know, certain sectors in the computing field actually have cultures that are very aggressive against women, and that is completely unacceptable. So there are many different actions that we can take at different levels and targeting women and people of different ages. But yeah, I think we definitely need to do something if we want to revert the situation. Yeah, no, no, thank you for sharing that. You covered so many of

the sort of challenges we face today. And, you know, I mean, and I'm really grateful that you actually shared many of the actions that you're taking that could possibly inspire our listeners to also pursue those opportunities in the areas of the organizations that they're in. One thing I've always believed is that you can't be what you don't see. And I know for a fact that the diverse set of opportunities that you have taken on and the fact that you share so much of your work and your journey with students and women across the world is inspiring in and of itself because I think as a young girl, anybody looking at like what can be achieved, looking at your journey will certainly feel more empowered to go and pursue that herself. This has been an amazing conversation, Nuria. For our final bite, I'd love to understand,

what are you most excited about in the field of data science and AI over the next few years? Well, I guess I'm really excited about what I've always been excited about, which is the huge power and the huge potential that we have to have positive societal impact through AI to really help people, all people, not just some people, you know, have a better quality of life, to really tackle the big challenges that we face from climate change to the energy crisis to the aging of the population, thanks to artificial intelligence or with the help of artificial intelligence. So I'm really motivated by all these opportunities, but I'm also cognizant and very much aware of the fact that that potential is has moved me into creating this nonprofit research foundation to

make sure that I do everything I can in contributing to making sure that AI is the best thing that happened to us and not the worst thing that happened to us. Wonderful. Thank you so much for sharing that and for the very inspiring conversation. We really are appreciative that you took the time to spend with us at ACM ByteCast. No problem. It was really a pleasure. Thank you for your interest and congratulations on your podcast. Thank you. ACM ByteCast is a production of the Association for Computing Machineries Practitioners Board. To learn more about ACM and its activities, visit acm.org. For more information about this and other episodes, please visit our website at learning.acm.org.

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