Assistant Professor of Data Science & Montreal Night Council Member , University of Virginia & MTL 24/24
Montreal & Charlottesville
Jess Reia is an Assistant Professor of Data Science at the University of Virginia. Before joining UVA, they were appointed Andrew W. Mellon Postdoctoral Researcher at McGill University (2019-2021) and 2020-2021 BMO Fellow at the Centre for Interdisciplinary Research on Montréal. They are currently a member of MTL 24/24’s Night Council and an AI Fellow at NewCities. From 2011 to 2019, they worked as Professor and Project Manager at the Center for Technology & Society at FGV Law School in Rio de Janeiro. Jess works on research and advocacy on nighttime governance, data justice, and tech policy in the Americas.
(In)visible Nights: Rethinking the limits of responsible technology for nightlife
The emergence of the Night Studies field over the last two decades brought to light a much-needed approach to how urban governance is applied to the 24-hour cycle. The urban night is a complex ecosystem encompassing data and regulation. Still, most cities lack a proper reflection or policies in place that address the intersection of technology and nightlife – even those that have developed comprehensive data-centric systems to understand the city in real time. From a public data perspective, the nightlife is often almost invisible, scattered across different datasets, portals and departments. But it is worth highlighting that the night is also a space for various marginalized communities whose exposure via open datasets might cause harm to people. The increased datafication and pressure to deploy cutting-edge technology – such as facial recognition and sensors – to improve safety appeal to the night industry. However, it is crucial to consider the various ethical, social, legal and economic issues that arise with implementing certain technologies. The work presented here combines expertise and case studies from multiple sectors in cities across North America and aims to contribute to night governance by answering three main questions: 1. How can we foster responsible uses of technology/ data in the nightlife industry? 2. What are the primary issues we need to address when deploying tech/data for the night? 3. How can we balance (in)visibilities in datasets for the night?