Next week (20th and 21st of April) we (TripleEd) have the great pleasure of welcoming Lauren Waardenburg to Umeå School of Business, Economics, and Statistics (USBE). Lauren is an assistant professor of Information Systems at IESEG School of Management. Lauren will give two talks, one at the department of Business administration, and one for the FrAIday seminars run by TAIGA – Centre for Transdisciplinary AI. Everyone is most welcome to both! If you need guidance on where to go, reach out to me (Markus).
FEK seminar serie
Thursday April 20,
Time: 13.30-14.30
Where: Green Room (dept of B.A)
Title: Juggling street work and data work: An ethnography of policing and reporting practices
Abstract: Organizational research on data production often aims to unpack the nature and meaning of data and the work practices through which it is made. Yet, not much is known about how the growing need to produce data influences the performance of other, more situated work. Our three-year ethnographic study of the Dutch police unravels this issue and shows that police officers adopt three strategies to cope with anticipated data work in their situated practices: avoiding work, deviating from protocol, and capturing experiences. These strategies helped police officers to alleviate the burden of data production, but also influenced how they performed their situated work and what and how crimes were reported, which contrasted the aims of data-driven police work. Our findings have implications for existing research on data production and for studies on anticipatory work by arguing that data construction starts at the situated practices and by showing how anticipating the work needed to produce data influences how both situated and data work are performed.
FrAIday seminar serie
Friday April 21
Time:11.00 – 12.00
WHERE: Hörsal C or Zoom (follow the link below)
Title: It takes a village: The ecology of explaining AI
https://www.umu.se/kalender/it-takes-a-village-the-ecology-of-explaining-ai-_11754070/
Abstract: AI systems are commonly believed to be able to aid in more objective decision-making and, eventually, to make objective decisions of their own. However, such belief is riddled with fallacies, which are based on an overly simplistic approach to organizational decision-making. Based on an ethnography of the Dutch police, we demonstrate that making decisions with AI requires practical explanations that go beyond an analysis of the computational methods used to generate predictions, to include an entire ecology of unbounded, open-ended interactions and interdependencies. In other words, explaining AI is ecological. Yet, this typically goes unnoticed. We argue that this is highly problematic, as it is through acknowledging this ecology that we can recognize that we are not, and never will be, making objective decisions with AI. If we continue to ignore the ecology of explaining AI, we end up reinforcing, and potentially even further stigmatizing, existing societal categories.