Prof. Polina Golland
Massachusetts Institute of Technology
From Pixels to Clinical Insight: Placental MRI Analysis Wednesday, 7 July, 15:15 CEST
Placental shape and function present many opportunities and challenges for medical image analysis. In this talk, I will discuss our work on extracting clinically relevant measures of placental function using geometric and statistical analysis approaches and discuss open problems in enabling clinical research in placental function via computational methods.
Biography: Polina Golland is a Henry Ellis Warren (1894) professor of Electrical Engineering and Computer Science at MIT and a principal investigator in the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL). Her primary research interest is in developing novel techniques for medical image analysis and understanding. With her students, Polina has demonstrated novel approaches to image segmentation, shape analysis, functional image analysis and population studies. She has served as an associate editor of the IEEE Transactions on Medical Imaging and of the IEEE Transactions on Pattern Analysis. Polina is currently on the editorial board of the Journal of Medical Image Analysis. She is a Fellow of the International Society for Medical Image Computing and Computer Assisted Interventions.
Prof. Bernhard Schölkopf
Max Planck Institute for Intelligent Systems
Toward causal learning for high dimensional observations Thursday, 8 July, 15:15 CEST
In machine learning, we use data to automatically find dependences in the world, with the goal of predicting future observations. Most machine learning methods build on statistics, but one can also try to go beyond this, assaying causal structures underlying statistical dependences. It turns out that causality can play a central role in addressing some of the hard open problems of machine learning, due to the fact that causal models are more robust to changes that occur in real world datasets. The talk will argue that causality has some shortcomings that are complementary to those of current machine learning, and the study of causal representation learning may help unify the advantages. It will also introduce some algorithms and applications in this field.
Biography: Bernhard Schölkopf's scientific interests are in machine learning and causal inference. He has applied his methods to a number of different fields, ranging from biomedical problems to computational photography and astronomy. Bernhard has researched at AT&T Bell Labs, at GMD FIRST, Berlin, and at Microsoft Research Cambridge, UK, before becoming a Max Planck director in 2001. He is a member of the German Academy of Sciences (Leopoldina), has (co-)received the J.K. Aggarwal Prize of the International Association for Pattern Recognition, the Academy Prize of the Berlin-Brandenburg Academy of Sciences and Humanities, the Royal Society Milner Award, the Leibniz Award, the Koerber European Science Prize, the BBVA Foundation Frontiers of Knowledge Award, and is an Amazon Distinguished Scholar. He is Fellow of the ACM and of the CIFAR Program "Learning in Machines and Brains", and holds a Professorship at ETH Zurich. Bernhard co-founded the series of Machine Learning Summer Schools, and currently acts as co-editor-in-chief for the Journal of Machine Learning Research, an early development in open access and today the field's flagship journal.
Prof. Bernd Stahl
De Montfort University Leicester
Artificial Intelligence for a Better Future - An Ecosystem Perspective on the Ethics of AI and Emerging Digital Technologies Friday, 9 July, 15:15 CEST
Smart information systems (SIS), those systems that incorporate artificial intelligence techniques, in particular machine learning and big data analytics, are widely expected to have a significant impact on our world. They raise significant hopes, for example to better understand and cure diseases, to revolutionize transport, to optimize business processes or reduce carbon emissions. At the same time, they raise many ethical and social concerns, ranging from worries about biases and resulting discrimination to the distribution of socio-economic and political power and their impact on democracy. A case in point is the discourse on the use of contact tracing apps during the novel coronavirus pandemic. Contact tracing has proven its effectiveness in disease containment for 500 years, but the application of advanced information technologies raises concerns about privacy, discrimination, and exclusion from essential public services to entirely new levels.
Drawing on the findings of the SHERPA project, the presentation will suggest a categorisation of the concept of smart information systems and a resulting categorisation of ethical concerns that these systems raise. It will suggest that one perspective to better understand these systems and their social and ethical consequences is to use the metaphor of an ecosystem to describe them, a metaphor already widely used in the policy discourse on AI. The talk will analyse what the use of the ecosystem metaphor means for the evaluation of ethical issues of smart information systems and which conclusions can be drawn from it and how these can inform recommendations for policymakers and other stakeholders.
The presentation is based on the material developed in a book with the same title, which is freely available.
Biography: Bernd Carsten Stahl is Professor of Critical Research in Technology and Director of the Centre for Computing and Social Responsibility at De Montfort University, Leicester, UK. His interests cover philosophical issues arising from the intersections of business, technology, and information. This includes the ethics of information and communications technology and critical approaches to information systems.