Co-Director LSTS, Vrije Universiteit Brussel (VUB), Belgium
Whiteboxing machine learning
The deployment of AI systems based on machine learning (ML) in real world scenarios faces a number of challenges due to its black box nature. The GDPR right to an explanation has given rise to frantic attempts to develop and design self-explanatory systems, meant to help people understand their decisions and behaviour. In this keynote I will explain (pun intended) why meaningful explanations require keen attention to the proxies used in ML research design. Once those confronted with the decisions or behaviour of ML systems have a better understanding of the pragmatic choices that must be made to allow a machine to learn, it will become easier to foresee what ML systems can and cannot do. Whiteboxing ML should focus on the proxies that stand for real world events, actions and states of affairs, highlighting that a proxy (dataset, variable, model) is not what it stands for.
Mireille Hildebrandt is a Research Professor on ‘Interfacing Law and Technology’ at Vrije Universiteit Brussels (VUB), appointed by the VUB Research Council. She is co-Director of the Research Group on Law Science Technology and Society studies (LSTS) at the Faculty of Law and Criminology.
She also holds the part-time Chair of Smart Environments, Data Protection and the Rule of Law at the Science Faculty, at the Institute for Computing and Information Sciences (iCIS) at Radboud University Nijmegen.
Her research interests concern the implications of automated decisions, machine learning and mindless artificial agency for law and the rule of law in constitutional democracies. Hildebrandt has published 5 scientific monographs, 23 edited volumes or special issues, and over 100 chapters and articles in scientific journals and volumes. She received an ERC Advanced Grant for her project on ‘Counting as a Human Being in the era of Computational Law’ (2019-2024), that funds COHUBICOL. In that context she is co-founder of the international peer reviewed Journal of Cross-Disciplinary Research in Computational Law, together with Laurence Diver (co-Editor in Chief is Frank Pasquale). In 2022 she has been elected as a Fellow of the British Academy (FBA).
University of Oxford, United Kingdom
Physics-inspired learning on graphs
The message-passing paradigm has been the “battle horse” of deep learning on graphs for several years, making graph neural networks a big success in a wide range of applications, from particle physics to protein design. From a theoretical viewpoint, it established the link to the Weisfeiler-Lehman hierarchy, allowing to analyse the expressive power of GNNs. We argue that the very “node-and-edge”-centric mindset of current graph deep learning schemes may hinder future progress in the field. As an alternative, we propose physics-inspired “continuous” learning models that open up a new trove of tools from the fields of differential geometry, algebraic topology, and differential equations so far largely unexplored in graph ML.
Michael Bronstein is the DeepMind Professor of AI at the University of Oxford and Head of Graph Learning Research at Twitter. He was previously a professor at Imperial College London and held visiting appointments at Stanford, MIT, and Harvard, and has also been affiliated with three Institutes for Advanced Study (at TUM as a Rudolf Diesel Fellow (2017-2019), at Harvard as a Radcliffe fellow (2017-2018), and at Princeton as a short-time scholar (2020)). Michael received his PhD from the Technion in 2007. He is the recipient of the Royal Society Wolfson Research Merit Award, Royal Academy of Engineering Silver Medal, five ERC grants, two Google Faculty Research Awards, and two Amazon AWS ML Research Awards. He is a Member of the Academia Europaea, Fellow of IEEE, IAPR, BCS, and ELLIS, ACM Distinguished Speaker, and World Economic Forum Young Scientist. In addition to his academic career, Michael is a serial entrepreneur and founder of multiple startup companies, including Novafora, Invision (acquired by Intel in 2012), Videocites, and Fabula AI (acquired by Twitter in 2019).