Science and Innovation

She is an international expert in Artificial Intelligence, a Telecommunications Engineer from the Polytechnic University of Madrid (UPM) and holds a PhD in Media Arts and Sciences from the Massachusetts Institute of Technology (MIT). In February 2024, the Academia Europaea Barcelona – Knowledge Hub (AE-BKH) announced that Nuria Oliver had won the Hypatia European Science Prize for her research into the development of new AI methods and systems to drive positive social impact. The award ceremony was organised by the Barcelona City Council in collaboration with the AE-BKH. We reproduce the interview that Academia Europaea – Cardiff Knowledge Hub held with the laureate.

Many congratulations on winning the prestigious Hypatia European Science Prize Could you share with us what receiving this award means to you?

Thank you very much. Receiving this award was a huge surprise and an  immense honor. This prestigious award means a lot to me for several  reasons.  First, because it highlights the importance of investing in scientific research  focused on having positive societal impact, which has been a driving force  throughout my career. Second, because it serves as an acknowledgement of  the continuous support and encouragement that I have received from my  family, colleagues, professors, mentors and collaborators. Without their  guidance, expertise and inspiration, none of this would have been possible.  Finally, the name of the award is very meaningful to me. Hypatia was a  prominent female philosopher, astronomer and mathematician who lived in  Alexandria over 2,000 years ago. Therefore, I am hoping that this award with  her name will help amplify the message of the need for inclusivity and  diversity in the technology sector in general and particularly in Artificial  Intelligence.  

You’ve spoken previously about your belief in the power of technology to  improve our quality of life, both individually and collectively. In your opinion,  what are the most promising applications of AI in addressing societal  challenges, such as healthcare, education, or environmental sustainability?

Yes, I am convinced that we need AI to address most of the immense  challenges that we face in the 21st century, from the energy crisis to climate  change and pandemics. Artificial Intelligence won’t be the solution but will  undoubtedly be part of the solution.  As an example, I can elaborate further on the interplay between AI and climate  change, and AI and health.  

Regarding climate change, AI methods based on machine learning – and  especially based on deep neural networks – allow us to model climate and  weather, identify patterns and make accurate predictions of changes in global  temperature by analyzing large amounts of multidimensional weather and  climate data. In addition to being used to build more accurate climate  predictions and models, AI methods can also be applied to improve next generation weather modeling systems by enabling, for example, the detection  and separation of noise in climate observations or the automatic labeling of  climate data.  

Extreme weather events – such as hurricanes, intense storms, floods… – are  increasing in frequency and intensity due to climate change. AI has also proven to be a valuable ally in predicting these extreme weather events and  their impact, and in enabling a more efficient and faster response to natural  disasters. Autonomous drones (guided by AI) can be used to prevent fires, or  to search for survivors in floods and earthquakes. In this area, the Artificial  Intelligence for Disaster Response (IADR) project at QCRI in Qatar provides a  free online tool that analyzes social media messages related to emergencies,  humanitarian crises and disasters. It uses AI techniques to automatically tag  thousands of messages per minute, acting as an early warning system. 

Beyond the direct application of Artificial Intelligence techniques to model  and predict the climate, AI methods can be applied to industries or sectors  that have a negative environmental impact to enable the reduction of  greenhouse gas (GHG) emissions. According to a report commissioned by  Microsoft from PwC, the use of AI in environmental use cases could  contribute up to $5.2 billion to the global economy by 2030 and reduce  greenhouse gas emissions by 4%, which is equivalent to the estimated 2030  annual emissions of Japan, Canada and Australia combined. 

We also rely on AI techniques to achieve more efficient renewable energies  (solar, wind…) thanks to the prediction of both the weather and energy  demand. And let’s not forget that it is impossible to have a smart energy  network (smart grid) without the help of AI. 

With respect to healthcare, Artificial Intelligence has the potential to bring  immense benefits to medicine in different areas, from accelerating the  discovery of drugs and treatments to improving diagnostic accuracy,  personalizing treatments or optimizing medical data management. 

AI techniques make it possible to perform virtual screening of compounds,  accelerating the identification of promising candidates and reducing the need  for costly and laborious experiments. In addition, AI algorithms can model  molecular interactions and predict the efficacy and safety of new potential  drugs, as well as predict the synthesis of chemical compounds, which helps  optimize the production of new drugs and reduces associated times and  costs. 

Thanks to AI, it can improve the design and planning of clinical trials by  identifying more effective inclusion/exclusion criteria, predicting treatment  response, and optimizing patient recruitment. 

Through the analysis of clinical and genomic data with AI techniques,  patterns, biomarkers and correlations about the effectiveness of treatments  and patient response can be discovered, treatments can be personalized and  adapted to the characteristics of each patient or existing medications to treat  different diseases can be identified.

AI algorithms can analyze large sets of medical data, such as magnetic  resonance imaging (MRI), CT scans, X-rays, and laboratory tests, to provide  faster and more accurate diagnoses. This can help healthcare professionals  identify diseases at early stages and improve treatment success rates. There  are numerous examples of AI algorithms that support the diagnosis of  different types of cancer, for example. 

Artificial Intelligence is also a key tool for more efficient management and  analysis of large amounts of medical data, helping professionals make  informed decisions and allowing the identification and reduction of possible  medical errors. For example, AI techniques enable the identification of  patterns in electronic medical records, the management of medical records,  and the prediction of epidemiological trends. In the context of public health,  the Data Science against COVID-19 working group that I led for more than 2  years is an example of the application of Artificial Intelligence techniques to  help combat a pandemic. 

Regarding surgery, Artificial Intelligence can assist surgeons. For example, in  robotic surgeries, AI can improve precision and enable more delicate  movements. 

AI can improve telemedicine by providing remote diagnostic tools and  medical advice based on data collected by mobile phones, sensors,  wearables and connected devices. Likewise, this data, analyzed with AI  techniques, can allow the detection of signs of clinical deterioration or  changes in a patient’s condition, enabling early intervention and risk  reduction. Robotic pets and other types of social robots are playing an  increasingly important role in combating loneliness in older people and  continuously monitoring their physiological signals and activity patterns. 

Obviously, as in other sectors, AI can be used to automate administrative  tasks, such as billing and scheduling, freeing up time for medical  professionals to focus more on direct patient care. There are also examples  of the use of chatbots to facilitate administrative procedures, resolve doubts  or even provide answers to simple medical questions. 

However, Artificial Intelligence systems are not perfect and pose technical,  ethical and social challenges that must be addressed to realize their immense  potential in all areas and especially in the field of medicine. We should  demand that AI systems applied to medicine comply with the FATEN  principles, described below.

The opportunities that AI research offers us to have positive social impact are  almost limitless. It is precisely this social aspect of AI what motivates and  has always motivated our work, and it is the focus of ELLIS Alicante. 

As a leader in AI research, what ethical considerations do you believe are  most crucial in the development and deployment of AI technologies?

I like summarizing the ethical considerations that we should demand from  any AI system with an acronym: FATEN.

F is for fairness or justice: Algorithmic decisions based on data can  discriminate, because the data used to train such algorithms may have biases  that result in discriminatory decisions; because of the use of a certain  algorithm; or due to the misuse of certain models in different contexts. We  should always demand algorithms that offer guarantees of non discrimination. 

A is for autonomy, a central value in Western ethics, according to which each  person should have the capacity to decide their own thoughts and actions,  ensuring therefore free choice, freedom of thought, and action. However,  nowadays we can build – and I have built – computational models of our  desires, needs, personality, and behavior with the ability to influence our  decisions and actions subliminally, as has become evident in recent electoral  processes in the US and the UK. We should ensure that intelligent systems  make decisions while always preserving human autonomy and dignity. A is also for accountability or attribution of responsibility, that is, being clear  about the attribution of responsibility for the consequences of algorithmic  decisions. And for augmentation of human intelligence, so that Artificial Intelligence  systems are used to enhance or complement human intelligence, not to  replace it. 

T is for trust, which is a basic pilar in the relationships between humans and  institutions. T is also for transparency to understand the reasons behind the decisions or  the behavior of the very complex neural networks that are at the core of most  AI systems today. Likewise, it is essential for Artificial Intelligence systems to  be transparent not only regarding what data they capture and analyze about  human behavior and for what purposes but also regarding the situations in  which humans are interacting with artificial systems (for example, chatbots)  versus with other humans.

E stands for education, meaning investing in education at all levels, starting  with compulsory education, but also education for citizens, professionals— especially those whose professions are being transformed by technology— public sector workers, and our political representatives. E is also for the principle of beneficence, i.e., maximizing the positive impact  of using Artificial Intelligence, with sustainability, diversity, honesty, and  truthfulness. Not all technological development entails progress. What we should aspire to  and invest in is progress. From my point of view, progress involves an  improvement in the quality of life of people –all people—, other living beings,  and our planet. 

N stands for non-maleficence, minimizing the negative impact that may  arise from the use of AI in our societies, applying a principle of prudence,  guaranteeing the security, reliability, and reproducibility of the AI systems, and  always preserving people’s privacy. 

It will only be when we respect these FATEN requirements that we will be able  to advance and achieve a socially sustainable Artificial Intelligence. 

You’ve shown dedication to making technology more accessible to non technical audiences, and inspiring young people (especially girls) to pursue  careers in technology. In your experience, how can we enhance diversity and  inclusion in the field of AI, both in terms of research and industry?

Enhancing diversity and inclusion in the field of AI is crucial for fostering  innovation, reducing bias, and ensuring that AI benefits everyone. Artificial  Intelligence is widely used in our society yet it is developed by homogeneous  groups that lack gender and other types of diversity. It is estimated than less  than 20% of AI experts in the world are women. This lack of diversity is  certainly negative not only for the field of AI but for society at large given the  ubiquity of AI in our lives, our businesses, our governments, and our societies.  

There are a variety of strategies that can help achieve this goal, starting with  education and outreach programs to inspire underrepresented groups,  including women and minorities, to study AI and related fields at an early age.  Examples include workshops, coding camps, and mentorship programs.  Community engagement can also be valuable to understand the needs,  concerns, and perspectives of diverse communities regarding AI and thus  foster a more inclusive development of AI systems.  

From a workplace perspective, there are several key actions to take. First, we  should implement diversity in hiring practices, by seeking out diverse candidates when hiring for AI research and industry positions. Furthermore,  the workplace culture should be inclusive such that everyone –and  particularly women and those belonging to minorities—feel valued and  supported. Diversity training, employee resource groups, and policies that  promote work-life balance and accommodate diverse needs are relevant  actions on this front. Finally, we also need to ensure diversity in leadership  positions within AI organizations and research institutions. Having diverse  voices at the decision-making table can lead to more inclusive policies and  practices. 

From an algorithmic perspective, we need to address biases in AI systems so  we can ensure fair and equitable outcomes that result from the use of such  systems for all populations. In ELLIS Alicante we have a research area  devoted to algorithmic fairness.  

These strategies could help make the field of AI more inclusive, diverse, and  reflective of the broader society it serves, leading to more innovative and  equitable outcomes.

Interview source: Academia Europaea – Cardiff Knowledge Hub