More and more decision making in society and politics is supported by intelligent and complex models (ICM). This project explores ways to better engage the electorate in this process by developing interpretable and explainable ICMs and informing the public about how these systems are used.
An increasing number of decisions with significant societal impact is supported by intelligent and complex models (ICMs). For instance, the government’s strategy for fighting the Covid pandemic is informed by complex models forecasting the spread of the virus. As these models thereby significantly impact our lives, there should be room to debate their usage and merits broadly within a democratic society. However, enabling this is non-trivial because (a) the precise workings of ICMs are typically hard to comprehend – for laymen as well as experts, and (b) it is often not made public how these systems are used within a political decision making process. This hinders the electorate’s evaluation of the political decision making and the made decisions with respect to an ICM’s usage: Whether and how to use an ICM? Which ICM to use?
We believe that the electorate can be better integrated into the democratic decision processes by building interpretable and explainable ICMs and informing the public about how these systems are used. Therefore, we propose to study the following research questions: (i) How can we build interpretable and explainable ICMs to improve transparency of decision making? How does this affect trust in made decisions? (ii) How to communicate decisions supported by ICMs to the public and how to integrate the public in the decision making process? By answering these questions we innovate our understanding of ICMs with the goal of supporting democratic processes.
Timothée's paper “On the Impact of Explanations on Understanding of Algorithmic Decision-Making” was accepted at ACM FAccT 2023!
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We are hiring for six exciting new PhD positions at the University of Vienna, Austria.
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Our project will be starting on the 1st of November 2021.
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Sebastian Tschiatschek is assistant professor for Machine Learning at the Faculty of Computer Science at the University of Vienna. He received his PhD from Graz University of Technology in 2014. After his PhD, he was a postdoctoral fellow at ETH Zurich and a senior researcher in the machine learning and perception group at Microsoft Research in Cambridge. He develops (probabilistic) machine learning algorithms for handling structured objects and sequential decision making. His research focuses on expressive models for heterogeneous data and on the impact of uncertainty and its quantification, for instance on sequential decision making and human-machine interaction. His research is published at top venues of machine learning, including the International Conference on Machine Learning (ICML), the Conference on Neural Information Processing Systems (NeurIPS) and the International Conference on Learning Representations (ICLR).
Torsten Möller is a professor of computer science at the University of Vienna, Austria, since 2013. Between 1999 and 2012 he served as a Computing Science faculty member at Simon Fraser University, Canada. He received his PhD in Computer and Information Science from Ohio State University in 1999 and a Vordiplom (BSc) in mathematical computer science from Humboldt University of Berlin, Germany. He is a senior member of IEEE and ACM, and a member of Eurographics. His research interests include algorithms and tools for analyzing and displaying data with principles rooted in computer graphics, human-computer interaction, signal processing, data science, and visualization.
Mark Coeckelbergh is a full Professor of Philosophy of Media and Technology at the Department of Philosophy and Vice Dean of the Faculty of Philosophy and Education of the University of Vienna. His expertise focuses on ethics and technology, in particular robotics and artificial intelligence. He is currently a member of various entities that support policy building in the area of robotics and artificial intelligence, such as the High Level Expert Group on Artificial Intelligence, which advices the European Commission, and the Austrian Robotics Council (Rat für Robotik, established by the Austrian Government to support policy building in the area of robotics), the Foundation for Responsible Robotics, and the IEEE Global Initiative for Ethical Considerations in Artificial Intelligence and Autonomous Systems. He is also former President of the Society for Philosophy and Technology (SPT), author of 12 philosophy books, and has been involved in interdisciplinary collaborations in the context of the European research projects INBOTS, DREAM (robot-enhanced therapy) and SATORI (ethical impact assessment of research and innovation).
As an academic philosopher, Eugenia (she/her) explores links between technological agency and subject agency – mostly around AI and care robots – to challenge concepts such as emotionality, accountability or care. She draws from the philosophy of technology as much as from (critical/political) media theory and posthuman approaches while her technological focus remains fluid. She increasingly includes socioeconomic, labour and discriminatory realities expressed through AI or care technology into her ethical exploration.
Eugenia has published on care robots, tracking and performative ethics; care robots on the theatre stage; the philosophy of screen bodies through the work of Flusser and Münsterberg; and on visual narratives of refugee camp life in documentary film.
After completing her PhD courses in PACT (Philosophy, Art & Critical Theory) at the European Graduate School in 2014, she was awarded a PhD fellowship as part of the Marie-Curie program CogNovo at the University in Plymouth situated in the Transtechnology Research group. In 2019, she submitted her PhD thesis on care robots, dataveillance and algorithmic accountability. Further, she holds a master’s degree (Diplom) in Media and Communication studies from the University of Arts in Berlin, and undertook undergraduate studies in Law, Art History and Literature at the Free University in Berlin.
Eugenia lectured in Media Arts; Digital Arts and Technology; and Architecture at the University of Plymouth. Besides being a postdoctoral researcher at the University of Vienna, Eugenia also consults for the Grüne Akademie and acts as a postdoctoral adviser for Transtechnology Research.
Her philosophical upbringing is strongly shaped by lingual and geographical fluidity, her father’s obsession with Michel de Montaigne and her mother’s socialist thinking. Besides all the things she would still like to do, she has finally accepted that she’ll never be a professional dancer.
In his PhD, Timothée Schmude works on the interface between Machine Learning and Social Sciences, researching avenues to render artificial intelligence interpretable. His background in Digital Humanities serves to connect conceptual and practical dimensions of the project, leading to a well-rounded estimation of potentials and challenges. He holds master’s degrees in Information Processing and Professional Writing, has experience in software development and interdisciplinary research and taught an introduction course about Deep Learning at the University of Cologne. He further was engaged in the Legal Tech Lab Cologne and developed a court sentence database prototype at the Fraunhofer Institute IAIS using Natural Language Processing.
Research Group Data Mining and Machine Learning, University of Vienna
Charlotte Zott
charlotte.zott@univie.ac.at
Währinger Straße 29, 1090 Vienna