Politics of Location Data, Mobilities, and Algorithmic Decision-Making

9th April 2020 @ 5:00 pm – 7:00 pm
University of Westminster (Room UG05)
309 Regent St
Marylebone, London W1B 2HT
Politics of Location Data, Mobilities, and Algorithmic Decision-Making @ University of Westminster (Room UG05) | England | United Kingdom
Didem Özkul (UCL) – Machine Learning on the Move: Politics of Location Data, Mobilities, and Algorithmic Decision-Making

The current global political and economic crisis has allowed location tracking and geo-profiling to grow exponentially, most prevalent in the developments of drones, commercial satellites, border security and geo-fencing, machine learning as well as being embedded in the form of a GPS sensor in every single smartphone produced and used. Location data is high dimension data, which has a very high degree of uniqueness associated with it, and hence it is difficult to anonymise. As users of these systems, we are potentially contributing to a network of sensors, generating real-time location data about where we are at any given point in time. Many sensors and connected devices also produce and use location data. So, the problem is not limited to ‘human’ users of these technologies’ but also other connected and algorithmic practices that somehow generate location data about us. Not surprisingly, efforts to generate a detailed picture of who we are based on where we are have been inherent in many machine learning and profiling algorithms. In this regard, location tracking provides a context for profiling algorithms and whether our movements can be flagged as behaviours of interest or the places we visit can be marked as places of interest. This type of profiling based on location tracking has the potential to categorise not only users, but also non-users of mobile communication technologies as well as places, in order to govern and control societies. The impacts of such algorithmic decision making are broader as they have the potential to create wider gaps and divisions within and between societies.

In this talk Didem Özkul draws on the problematic intersection of machine learning, algorithmic decision-making, and politics of mobilities with a specific focus on location data, and argues that location data is key to all those other types of data. In doing so, she discusses the societal implications of location data and machine learning, which she identifies as a gap that we need to attend to urgently.


Didem Özkul (@didemozkul)is an Assistant Professor in Digital Media & Society at the Department of Culture, Communication and Media, University College London. She holds a PhD from the Communication and Media Research Institute, University of Westminster. She has written extensively about location data and mobile communication and media practices. Currently she is writing her first monograph, The Politics of Location Tracking and Profiling, which presents a critical analysis and discussion of location data, AI, and politics of mobilities (under contract with Routledge).

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