Panel Discussion
Can Network Science Bridge the Gap Between AI and Responsible Decision-Making?
The field of Network Science (aka Complex Networks) has demonstrated remarkable capabilities in understanding and modelling complex systems through their relational and structural properties. As Artificial Intelligence (AI) becomes increasingly integrated into critical decision-making processes across society, we face significant challenges regarding ethical considerations, transparency, and the handling of subjective, contextual information. Current AI systems, whilst powerful, often struggle with these aspects, potentially leading to biased or inappropriate decisions with serious consequences for individuals and society. Traditional AI approaches frequently lack the ability to consider ethical implications, explain their reasoning, or adapt to changing contexts; limitations that Network Science, with its rich theoretical framework and tools for analysing complex interactions, might help address.
For instance, in healthcare, AI-assisted diagnostics have shown promise but raised concerns about their ability to consider individual patient contexts and explain their recommendations to healthcare professionals. Similarly, in the legal domain, AI tools for predicting case outcomes or assisting in sentencing have been criticised for potentially perpetuating systemic biases. Network Science, with its established methodologies for studying complex interdependencies and emergent behaviours, offers unique perspectives and tools for addressing these challenges. Through Network Science techniques, we might better understand and model the complex web of relationships between decisions, their contexts, and their ethical implications.
For instance, in healthcare, AI-assisted diagnostics have shown promise but raised concerns about their ability to consider individual patient contexts and explain their recommendations to healthcare professionals. Similarly, in the legal domain, AI tools for predicting case outcomes or assisting in sentencing have been criticised for potentially perpetuating systemic biases. Network Science, with its established methodologies for studying complex interdependencies and emergent behaviours, offers unique perspectives and tools for addressing these challenges. Through Network Science techniques, we might better understand and model the complex web of relationships between decisions, their contexts, and their ethical implications.
Panel Speakers
Panellist's Bios
Fernando Buarque is PhD in AI (Imperial College London-2002), Senior Associate Professor and AI research head at the University of Pernambuco-Brazil, Alexander von Humboldt Fellow and Pernambuco Academy of Science life-peer. He supervised over 150 research students, authored circa 300 scientific publications, his current research tackles complex decision & optimization problems via explainable and responsible evolutionary and social modelling/simulations. Prof Buarque thinks Responsible-AI can lead to flourishing societies for the planetizens.
Elsa Estevez is the chair holder of the UNESCO Chair on Knowledge Societies and Digital Governance at Universidad Nacional del Sur, Principal Researcher at the National Council of Scientific and Technical Research, and Full Professor at the National University of La Plata, all in Argentina. She is also a consultant for IADB on the matters of digital government, particularly in Latin America. Previously, she was a Senior Academic Program Officer at the United Nations University in Macao and Portugal; Visiting Professor at the National University of Rio Negro, Argentina; Gdańsk University of Technology, Poland; University of Minho, Portugal; and head of technology departments in large financial and pharmaceutical organizations in Argentina. Her research focuses on digital government, sustainable development, smart sustainable cities, and citizen participation.
Marta Gonzalez is an Associate Professor of Civil and Environmental Engineering and City and Regional Planning at UC Berkeley, and also a Physics Research faculty in the Energy Technology Area (ETA) at the Lawrence Berkeley National Laboratory (Berkeley Lab). Gonzalez’s research focuses on urban sciences, with a focus on the intersections of people with the built and the natural environment and their social networks. In 2023, she was named fellow of the Network Science Society for her seminal contributions to our understanding of human mobility and transportation networks, and for applying network modelling to solve societal problems in urban systems, and in 2024 she received the Lagrange-CRT Foundation Prize for her scientific research in the field of complexity sciences, its applications and dissemination.
César Lincoln Cavalcante Mattos is an associate professor at the Department of Computer Science, at Federal University of Ceará (UFC), Brazil. He is an associate researcher at the Logics and Artificial Intelligence Group (LOGIA). He is a member of the graduate programs in Computer Science (MDCC/UFC) and in Modelling and Quantitative Methods (MMQ/UFC). He has research interests in the broad fields of machine learning and probabilistic modelling, such as Gaussian processes, deep (probabilistic) learning, approximate inference and system identification. Furthermore, he has been applying machine learning methods in several research and development collaborations, in areas such as dynamical system modelling, health risk analysis, software repository mining and anomaly detection.
JF Mendes earned his PhD in Physics from the University of Porto in 1995, with subsequent research positions at prestigious institutions including the Universities of Geneva, Oxford, and Boston before becoming a Full Professor at the University of Aveiro in 2005. Internationally recognized as a pioneer in complex networks research, he developed groundbreaking analytical solutions for "small-world" and "scale-free" network models, authored the first textbook on Complex Networks published by Oxford University Press, and produced over 170 scientific articles with thousands of citations. His contributions span phase transitions, epidemic propagation, and emergent phenomena in complex systems, while his leadership extends to administrative roles as Vice-Rector for Research at the University of Aveiro and coordinator of numerous European and national projects. His achievements have garnered prestigious recognitions, including the Calouste Gulbenkian Foundation Prize (2004), the Complex Systems Society Senior Prize (2020), and his election as a Fellow of the American Physical Society (2020).
Elsa Estevez is the chair holder of the UNESCO Chair on Knowledge Societies and Digital Governance at Universidad Nacional del Sur, Principal Researcher at the National Council of Scientific and Technical Research, and Full Professor at the National University of La Plata, all in Argentina. She is also a consultant for IADB on the matters of digital government, particularly in Latin America. Previously, she was a Senior Academic Program Officer at the United Nations University in Macao and Portugal; Visiting Professor at the National University of Rio Negro, Argentina; Gdańsk University of Technology, Poland; University of Minho, Portugal; and head of technology departments in large financial and pharmaceutical organizations in Argentina. Her research focuses on digital government, sustainable development, smart sustainable cities, and citizen participation.
Marta Gonzalez is an Associate Professor of Civil and Environmental Engineering and City and Regional Planning at UC Berkeley, and also a Physics Research faculty in the Energy Technology Area (ETA) at the Lawrence Berkeley National Laboratory (Berkeley Lab). Gonzalez’s research focuses on urban sciences, with a focus on the intersections of people with the built and the natural environment and their social networks. In 2023, she was named fellow of the Network Science Society for her seminal contributions to our understanding of human mobility and transportation networks, and for applying network modelling to solve societal problems in urban systems, and in 2024 she received the Lagrange-CRT Foundation Prize for her scientific research in the field of complexity sciences, its applications and dissemination.
César Lincoln Cavalcante Mattos is an associate professor at the Department of Computer Science, at Federal University of Ceará (UFC), Brazil. He is an associate researcher at the Logics and Artificial Intelligence Group (LOGIA). He is a member of the graduate programs in Computer Science (MDCC/UFC) and in Modelling and Quantitative Methods (MMQ/UFC). He has research interests in the broad fields of machine learning and probabilistic modelling, such as Gaussian processes, deep (probabilistic) learning, approximate inference and system identification. Furthermore, he has been applying machine learning methods in several research and development collaborations, in areas such as dynamical system modelling, health risk analysis, software repository mining and anomaly detection.
JF Mendes earned his PhD in Physics from the University of Porto in 1995, with subsequent research positions at prestigious institutions including the Universities of Geneva, Oxford, and Boston before becoming a Full Professor at the University of Aveiro in 2005. Internationally recognized as a pioneer in complex networks research, he developed groundbreaking analytical solutions for "small-world" and "scale-free" network models, authored the first textbook on Complex Networks published by Oxford University Press, and produced over 170 scientific articles with thousands of citations. His contributions span phase transitions, epidemic propagation, and emergent phenomena in complex systems, while his leadership extends to administrative roles as Vice-Rector for Research at the University of Aveiro and coordinator of numerous European and national projects. His achievements have garnered prestigious recognitions, including the Calouste Gulbenkian Foundation Prize (2004), the Complex Systems Society Senior Prize (2020), and his election as a Fellow of the American Physical Society (2020).
