Shuyi Li: Understandings of explainable AI (XAI) in research communities. A mixed-method approach using topic modelling and SNA

The Department of Digital Humanities and Social Studies would like to invite you to the following talk in our DH Colloquium:

Shuyi Li: »Understandings of explainable AI (XAI) in research communities. A mixed-method approach using topic modelling and SNA«

Abstract

Machine learning (ML) is now ubiquitous in daily life and has a far-reaching impact on society. Researchers in Explainable Artificial Intelligence (XAI) strive to develop explainable machine learning techniques to mitigate the adverse effects of black-box models. XAI research inherently requires interdisciplinary collaboration. In light of this, this thesis conducts both qualitative and quantitative analyses of literature from the humanities, psychology, and STEM fields to (1) explore the extent to which diverse research domains remain isolated from one another, and (2) evaluate both historical and recent influential algorithms from a multidisciplinary perspective.

Further information and other upcoming talks in the Colloquium can be found here.