On Monday the 29th of September, Hannah Devinney presented the EQUITBL project at the AI for Good Breakthrough Days main stage event. The project is one of three winners in the Breakthrough Days’ Gender Equity Track.
This interdisciplinary project explores ways of combining qualitative and quantitative methods in order to explore and understand how bias and stereotypes manifest themselves in large text collections, such as those commonly used to train machine learning models in language technology. We also develop tools for mitigating the detrimental effects bias, stereotyping, and underrepresentation can have when the ML models are integrated into AI systems used for decision making.
The project members are:
Hannah Devinney, Computing Science, Centre for Gender Studies, and LPCN, Umeå University
Henrik Björklund, Computing Science and LPCN, Umeå University
Jenny Björklund, Centre for Gender Research, Uppsala University
In the afternoon of March 20, a number of LPCN members had a workshop together with Humlab on the topic of text analysis. We all presented how we use or plan to use text analysis in our work. Happily, we managed to identify a number of immediate collaboration opportunities and also discussed the possibilities for building a joint text analysis infrastructure. As an additional benefit, the LPCN family grew substantially! The workshop concluded with a very nice after work session.