Brown Bag Seminar: “Untrustworthy” workers and “ground truth”

Ground-truth datasets are supposed to nail down facts about the “world”, so that machine-learning models trained on them will behave reliably in that same world. Yet when annotation is outsourced to crowdworkers whom engineers do not know, and often distrust, how is such reliability achieved or even imagined? Based on 27 interviews with machine learning researchers, crowdsourcing practitioners, and industry insiders, this research investigates how ground-truth datasets are stabilised.

This seminar’s speaker is Laura Savolainen, a postdoctoral researcher working at the intersection of sociology and science and technology studies. Her research examines the social and political dimensions of algorithmic systems

The Methodological unit at the Helsinki Institute for Humanities and Social Sciences (Uni Helsinki) organizes a recurrent Brown Bag Seminar to highlight novel methodological approaches in the humanities and social sciences. The idea of the meetings is to introduce methodological innovations and cutting-edge research in various disciplines in an easily accessible manner and have an interdisciplinary discussion in an easy-going atmosphere over lunch.

There will be a 20-minute introduction to the methodological theme, followed by an open discussion of 40 minutes. The seminars are open to everybody and welcome a multidisciplinary and methodologically curious audience. The language of the meetings can be Finnish or English.

This is a hybrid event taking place at the University of Helsinki, Fabianinkatu 24 A, 5th floor, room 531. For remote participation, use this link.

More information is available on the event site.