A successful grant application is a crucial component in the development of a researcher. Multiple studies have found biases in grant reviews that favour certain groups of researchers. These attitudes reduce the quality of research outcomes and contribute to a cyclic disadvantage to underrepresented groups, particularly in STEM.

Current efforts to overcome this issue have proven insufficient as bias in our judgement seems deeply ingrained in human behaviour. NLP is a promising interdisciplinary field with successful applications in analysing text strings without introducing bias.

This project proposes to evaluate the feasibility and effectiveness of NLP algorithms in assessing grant applications by detecting text-based criteria that define the success of an application and identifies bias-related trends.

By developing a proof of concept on the use of NLP to assess grant applications, this project outsets the foundation for the development of a tool that assists founding entities in having a more inclusive and fair review system.

Flexible funding: FA2 round 1 2023

Watch Dr Gloria Castro Quintero's (University of Strathclyde) presentation at the IGNITE Annual Event 2024