Through three episodes in collaboration with FemData, this series unpacks the hidden impacts of data bias, examines the power dynamics driving AI systems and explores policies and initiatives shaping its future. This series seeks to explore building equitable, inclusive and responsible AI technologies that benefit everyone. Join us as we navigate the challenges and opportunities of an AI-driven world.
As artificial intelligence increasingly shapes our society, critical questions arise about accountability and power. In this second episode we aim to discuss who controls the data and algorithms driving these systems? How do these power dynamics influence societal structures, and in what ways do they perpetuate patterns of inequality? In this programme, we’ll delve into the forces behind AI and the societal structures they influence.
About Speakers
Daniel Mügge is Professor of Political Arithmetic at the University of Amsterdam (UvA). As leader of the NWO Vici project RegulAite, he investigates how the EU governs artificial intelligence and how these politics are shaped by global geopolitical and economic competition. At the UvA, he is also co-founder of the research platform and the research priority area AI & Politics. Before starting his work on AI, Daniel explored the political underbelly of macroeconomic statistics with his FickleFormulas project. A political economist by training, he has been a visiting researcher at Harvard’s Center for European Studies, the London School of Economics and the Freie Universität Berlin, his alma mater.
Alexander Laufer works as researcher and policy officer on technology and discrimination at Amnesty International. He has worked several years on discriminatory risk-profiling, with a particular focus on racial profiling. A former data scientist, Alexander has an interdisciplinary background with degrees in economics, information science and neuroscience.
About FemData
FemData is an initiative founded by Myrthe Blösser and Paulina von Stackelberg, two PhD researchers at the University of Amsterdam. Committed to addressing data bias through interdisciplinary approaches, FemData organizes a variety of educational and outreach events. These events are designed to foster meaningful discussions and bring together diverse perspectives to tackle the challenges of bias in data science.
