About the project
A major concern when dealing with complex machine learning models is how to determine what influences the outcome of such models, the well-known explainability challenge. There have been several approaches to extract interpretable abstractions from machine learning models, in particular Large Language Models (LLMs). These approaches greatly contribute to effectively detecting useful information from language models, such as harmful biases. However, they often tackle this issue from a purely practical point of view and therefore lack theoretical guarantees. This project aims to employ techniques based on computational learning theory to extract rules potentially expressing harmful biases from language models, while providing theoretical guarantees for the rules extracted.
Project team
Associate Professor Ana Ozaki, University of Oslo, Department of Informatics
Professor Lilja Øvrelid, University of Oslo, Department of Informatics
Associate Professor Samia Touileb, University of Bergen, Department of Information Science and Media Studies
PhD Fellow Emil Poiesz, University of Oslo, Department of Informatics