Ida Marie S. Lassen will defend her dissertation From Biased Models to Critical Practice: How Machine Learning and Humanities Practices Transform Each Other.

The dissertation will be available for reading in a digital version before the defence following a statement from the borrower promising to delete the file afterwards. If you wish to read the dissertation please contact Ida Marie S. Lassen <idamarie@cas.au.dk>

The defence is scheduled for three hours and is open to the public. All are welcome.

Assessment Committee

• Associate Professor Joshua Skewes, School of Communication and Culture, Aarhus University (chair)
• Winship Distinguished Research Professor Lauren Klein, Department of Data & Decision Sciences and Department of English, Emory University
• Professor Anders Kristian Munk, DTU Management, Technical University of Denmark

Supervisors

• Professor Kristoffer Nielbo, Centre for Humanities Computing, Aarhus University
• Professor Jens Christian Bjerring, Philosophy, Aarhus University

Abstract

This dissertation explores the epistemological dimensions of machine learning in the Digital Humanities, examining how computational methods shape knowledge production, introduce biases, and interact with our interpretive pathways. By combining practical engagement with machine learning tools and theoretical reflection informed by philosophy of science, the dissertation analyses how critical practices can emerge from within computational workflows and how humanities inquiry and digital methods mutually inform one another.