Insights & Ideas
from Experts in the Field
Next Lecture:
Wednesday, July 9th, 6:30 pm
Location: Plenarsaal of the Bavarian Academy of Sciences and Humanities (BAdW), Alfons-Goppel-Straße 11, 80539 Munich
Registration will be available soon!
Prof. Virginia Dignum - Responsible AI: Governance, Ethics, and Sustainable Innovation
Abstract
As AI systems become increasingly autonomous and embedded in socio-technical environments, balancing innovation with social responsibility grows increasingly urgent. Multi-agent systems and autonomous agents offer valuable insights into decision-making, coordination, and adaptability, yet their deployment raises critical ethical and governance challenges. How can we ensure that AI aligns with human values, operates transparently, and remains accountable within complex social and economic ecosystems? This talk explores the intersection of AI ethics, governance, and agent-based perspectives, drawing on my work in AI policy and governance, as well as prior research on agents, agent organizations, formal models, and decision-making frameworks. Recent advancements are reshaping AI not just as a technology but as a socio-technical process that functions in dynamic, multi-stakeholder environments. As such, addressing accountability, normative reasoning, and value alignment requires a multidisciplinary approach. A central focus of this talk is the role of governance structures, regulatory mechanisms, and institutional oversight in ensuring AI remains both trustworthy and adaptable. Drawing on recent AI policy research, I will examine strategies for embedding ethical constraints in AI design, the role of explainability in agent decision-making, and how multi-agent coordination informs regulatory compliance. Rather than viewing regulation as a barrier, will show that responsible governance is an enabler of sustainable innovation, driving public trust, business differentiation, and long-term technological progress. By integrating insights from agent-based modeling, AI policy frameworks, and governance strategies, this talk underscores the importance of designing AI systems that are both socially responsible and technically robust. Ultimately, ensuring AI serves the common good requires a multidisciplinary approach—one that combines formal models, ethical considerations, and adaptive policy mechanisms to create AI systems that are accountable, fair, and aligned with human values.
Bio
Virginia Dignum is Professor of Responsible Artificial Intelligence at Umeå University, Sweden, where she leads the AI Policy Lab. She is also senior advisor on AI policy to the Wallenberg Foundations and chair of the ACM’s Technology Policy Council. She has a PHD in Artificial Intelligence from Utrecht University (2004), was appointed Wallenberg Scholar in 2024, is member of the Royal Swedish Academy of Engineering Sciences (IVA), and a Fellow of the European Artificial Intelligence Association (EURAI), and of ELLIS (European Laboratory of Learning and Intelligent Systems). She is also co-chair of the IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems 2.0, member of the Global Partnership on AI (GPAI), of the UNESCO’s expert group on the implementation of AI recommendations, the OECD’s Expert group on AI, and founder of ALLAI, the Dutch AI Alliance. She has been a member of the United Nations Advisory Body on AI, the EU’s High Level Expert Group on Artificial Intelligence, co-chair of the WEF’s Global Future Council on AI, and leader of UNICEF’s guidance for AI and children. Her new book “The AI Paradox” is planned for publication in 2025.
Latest Lecture:
Prof. Michael Mahoney
On a monthly basis, we invite top-level AI researchers to give us a glimpse into their work and the future of AI. Join us for our next lecture!
Our lectures consist of a short presentation followed by a Q&A to enable a lively discussion with our speakers.
Join Us!
Monday, November 25th, 5:15 pm
Location: Große Aula, Raum E 120, 1. OG, Geschwister-Scholl-Platz 1, 80539 München, LMU München
This event is open to everyone, registration is not required.
Insights & Ideas
from Experts in the Field
Helmut Bölcskei - The Mathematical Universe behind Deep Neural Networks
Abstract
Deep neural networks have led to breakthrough results in numerous practical machine learning tasks. In this lecture, we will attempt a journey through the mathematical universe behind these practical successes, elucidating the theoretical underpinnings of deep neural networks in functional analysis, harmonic analysis, complex analysis, approximation theory, dynamical systems, Kolmogorov complexity, optimal transport, fractal geometry, mathematical logic, and automata theory.
Bio
Helmut Bölcskei is a professor of Mathematical Information Science at ETH Zurich. Since 2021 he has also been a Principal Investigator at the Lagrange Mathematics and Computing Research Center, Paris, France.
He received his degrees from Vienna University of Technology, Vienna, Austria, was a postdoctoral researcher in the Information Systems Laboratory, Department of Electrical Engineering, and in the Department of Statistics, Stanford University, Stanford, CA. He was in the founding team of Iospan Wireless Inc., a Silicon Valley-based startup company (acquired by Intel Corporation in 2002) specialized in multiple-input multiple-output (MIMO) wireless systems for high-speed Internet access, and was a co-founder of Celestrius AG, Zurich, Switzerland. He was a visiting researcher at Philips Research Laboratories Eindhoven, The Netherlands, ENST Paris, France, and the Heinrich Hertz Institute Berlin, Germany. His research interests are in applied mathematics, machine learning theory, mathematical signal processing, data science, and statistics.
He received the 2001 IEEE Signal Processing Society Young Author Best Paper Award, the 2006 IEEE Communications Society Leonard G. Abraham Best Paper Award, the 2010 Vodafone Innovations Award, the ETH “Golden Owl” Teaching Award, is a Fellow of the IEEE, a 2011 EURASIP Fellow, was a Distinguished Lecturer (2013-2014) of the IEEE Information Theory Society, an Erwin Schrödinger Fellow (1999-2001) of the Austrian National Science Foundation (FWF), was included in the 2014 Thomson Reuters List of Highly Cited Researchers in Computer Science, was the 2016 Padovani Lecturer of the IEEE Information Theory Society, and received a 2021 Rothschild Fellowship from the Isaac Newton Institute for Mathematical Sciences, Cambridge University, UK. He served as editor-in-chief of the IEEE Transactions on Information Theory and is the founding editor-in-chief of the Springer journal “Mathematical Foundations of Machine Learning”. He has been a delegate for faculty appointments of the president of ETH Zurich since 2008.