Andreas Kaltenbrunner, lead researcher of the UOC AI and Data for Society group
Artificial intelligence (AI) and data have the potential to change the world. A recent study by Goldman Sachs suggests that globally 18% of jobs could be automated using AI. This technology has the potential to transform many aspects of our lives, including the ways we get information, express ourselves and communicate. So, it is crucial to understand its relationship with the social sciences and humanities.
This is one of the goals of Andreas Kaltenbrunner, a researcher who is exploring the more human side of how these new technologies are being applied. After working at different European technology and research centres, he has come to the Universitat Oberta de Catalunya (UOC) to lead a new research group at the Internet Interdisciplinary Institute (IN3): AI and Data for Society (AID4So).
This group will focus on developing new methods for AI, machine learning and big data analytics, while also seeking ways to apply their research in areas such as the social sciences and digital humanities. We spoke to Kaltenbrunner to find out more about his career and the objectives of this research group, which analyses technology while placing the human being at the centre.
Your career has been focused on leading teams working with AI and data, with the goal of better understanding human behaviour. What made you interested in this field?
My scientific goal has always been to better understand human behaviour, both at the individual and societal levels, and I believe it is important to use the most advanced methods and data sources possible to make progress in this field.
To pursue my training in this field, I did a master's degree in Mathematical Computer Science at the Vienna University of Technology (TU Wien) and then a PhD in Computer Science and Digital Communication at Pompeu Fabra University (UPF).
How did your path lead you to the UOC?
After finishing my PhD at the UPF in 2008, I went on to work at the Barcelona Media technology centre. While I was there, I took part in creating the line of research into social media, which I went on to lead in May 2013. Between 2015 and 2017, I was scientific director of the Digital Humanities Research Unit at the Eurecat technology centre, and I joined NTENT in September 2017 as director of Data Analytics.
In October 2020, I started working as a senior research scientist at the ISI Foundation in Turin, where I stayed until I joined the UOC. At the ISI Foundation, I used methods from computational science, AI and complex systems to address sociological and technological research questions.
And what is your research experience?
I now have over 10 years of experience leading research and data science groups and have co-supervised doctoral and master's degree students. I've been involved in obtaining public funding for a number of research projects: as a supervisor for the SAFRAN Marie Skłodowska-Curie Action Grant, as coordinator of the European Contropedia project and as principal investigator of a project partner in European projects and Spanish national technology transfer projects (from the CENIT programme) on social media and big data.
I've also worked as a principal investigator in technology transfer projects with Intesa Sanpaolo, Orange Spain and Barcelona City Council, and been involved in the first nationwide study on the use of social networks in Spain, commissioned by the state-owned enterprise red.es.
Throughout your career, you have published numerous research articles. Can you tell us about this aspect of your work?
Thanks to my participation in conferences, workshops and different European-funded projects, I've been able to establish a strong network for international collaboration. I've published articles with co-authors from the universities of Oxford and Cambridge, the Paris Institute of Political Studies (Sciences Po), the University of Amsterdam, the Polytechnic University of Milan and the University of Toulouse, among others.
I've published 23 articles in JCR-indexed journals and another 19 in leading peer-reviewed conferences, such as CHI or SIGKDD. I've won the best paper award on two occasions. Likewise, I'm a member of the senior programme committees of conferences such as TheWebConf and CIKM, and the editorial board of the Elsevier journal Online Social Networks and Media.
I have also served on five thesis committees and been a reviewer for Horizon 2020 and COST proposals for the European Commission, and for more than 20 different conferences, workshops and indexed journals.
Which of your research projects have you found most interesting or different?
That's a difficult question. The most interesting is always the latest one, but I could also highlight the one related to the article on "Societal Controversies in Wikipedia Articles". The study was the result of a project where interactions between editors on Wikipedia were used to determine the most controversial aspects of a topic (such as climate change).
I'm currently reusing this research in a collaboration with the Wikimedia Foundation to develop a reliability index for the websites that are used as sources.
And what is the most relevant research for the work that the new AI and Data for Society (AID4So) group will undertake?
One example is a study that has recently been published: "Large scale analysis of gender bias and sexism in song lyrics". In this study, we used AI methods to analyse the lyrics of more than 375,000 songs and measure the evolution of sexism and gender bias over six decades.
We found that the proportion of sexist lyrics increased over time, especially among songs by male artists and those in the Billboard charts. We also found that songs contain different linguistic biases depending on the gender of the performer, with songs by male artists having stronger biases.
This study offers insights into the use of language in a very influential part of popular culture: music. It is the first large scale analysis of its kind.
What other areas will this new IN3 group focus on?
The group will focus on the development of new methods in AI, machine learning and big data analytics as well as their application to solve research questions in data science, computational social science and digital humanities.
The group puts the human being at the centre of its research efforts and therefore focuses on the study and use of ethical, sustainable and explainable methods.
What are its objectives?
The group has a range of objectives. One of them focuses on theoretical research in AI and machine learning. In this field, we are working to improve and develop new methods, particularly methods that are explainable in text generation and classification, information credibility detection and new embedding techniques in graph neural networks.
Another of the group's main objectives revolves around applied research in data science. Using state-of-the-art methods from computational science, AI and machine learning, we're working to deepen our understanding of human behaviour, on individual, group and societal scales, both in the digital and offline world.
How important is it to harness AI and the use of data to solve societal challenges?
It is very important, but it also creates new challenges. It has the potential to be (and already is) disruptive in many areas. There are many types of work and sectors that are and will be affected, and many technologies that can be used to improve society as well as to make it worse.
In this sense, AI and the use of data can help us find solutions to societal challenges. For example, by facilitating informed decision-making, showing possible improvements in the efficiency of social programmes or providing new ways to promote social innovation.
However, it is essential to consider the ethics, equity and social impact of these solutions in order to prevent them from being misused, having negative and unintended side-effects or being rejected by society.
What does it mean for the UOC to have a research group like this?
The UOC and particularly the IN3 are perfect for hosting this research group, as its focus is the study of the effect of the internet and ICT on different areas of human activity. There are many possibilities for collaborations and synergies with existing research groups.
The approach of the research group will be interdisciplinary. We will use and develop basic computer science methods and algorithms for artificial intelligence, machine learning and big data analysis, while seeking to make them applicable to the social sciences and humanities.
This effort will not be one way: the group will seek collaborations in the fields of social sciences and humanities to help improve AI algorithms. In order to do this, it will place the human being at the centre, working on methods that enable and improve human understanding and acceptance of AI results.
Ultimately, this will enable the UOC to be an active and positive agent at the intersections between AI and everyday human activities and tasks.
The UOC's research and innovation (R&I) is helping overcome pressing challenges faced by global societies in the 21st century by studying interactions between technology and human & social sciences with a specific focus on the network society, e-learning and e-health.
Over 500 researchers and more than 50 research groups work in the UOC's seven faculties, its eLearning Research programme and its two research centres: the Internet Interdisciplinary Institute (IN3) and the eHealth Center (eHC).
The university also develops online learning innovations at its eLearning Innovation Center (eLinC), as well as UOC community entrepreneurship and knowledge transfer via the Hubbik platform.
Open knowledge and the goals of the United Nations 2030 Agenda for Sustainable Development serve as strategic pillars for the UOC's teaching, research and innovation. More information: research.uoc.edu.