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