Talk by Dr. Linnet Taylor, Tilburg Institute for Law, Technology and Society
Biography
Linnet Taylor is an associate Professor at the Tilburg Institute for Law, Technology, and Society (TILT), where she leads the ERC-funded Global Data Justice project, seeking to understand the different perspectives worldwide on what constitutes just treatment through data technologies. Her research focuses on the use of new sources of digital data in governance and research around issues of human and economic development. She was previously a Marie Curie research fellow in the University of Amsterdam’s International Development faculty, and a postdoctoral researcher at the Oxford Internet Institute. She holds a DPhil in International Development from the Institute of Development Studies, University of Sussex.
Abstract
Europe has placed much trust in both data protection regulation and ethics to solve problems of exploitation and unfairness in relation to data technologies. However, it has become apparent that data protection does not tackle the structural components of the problem, and ethics-washing has become a highly visible problem in relation to the private sector in particular. Moreover, we are seeing that attempts to formalise values such as fairness, accountability and transparency into requirements for computing systems are failing to actually embed those values in the governance of technology. I will argue that if we wish to regain control over our data economy we should focus our attention on concerns of social justice, politics and the global scale of data exploitation. The data economy is regulated nationally but operates across borders, and the scale of the problem has outpaced regulatory authorities’ capacity around the world. This presentation will consider the value of a social justice approach to data governance: one that can connect the problems of data injustice to those of injustice more broadly. This global data justice perspective offers a more structural and political take on problems that are usually tackled on too local, or too technical a scale, such as algorithmic bias, fairness and transparency. It does the essential work of demonstrating how they play out similarly across the world, and how this problem cannot be tackled meaningfully by high-income countries alone.
Information
- Time and place: at 4:15 pm, room BC 420, EPFL
