Amid massive protests across the US against systemic racial bias, researchers have sought to analyse data on police encounters with civilians to draw conclusions about the extent and nature of such racism in the criminal justice system. Dean Knox of Princeton University, Will Lowe of the Hertie School, and Jonathan Mummolo of Princeton University argued in a paper published in the American Political Science Review (APSR) in May that some high-profile research is skewed due to bias inherent in data sampling. This means that some research may even be underestimating the level of racial bias in the criminal justice system. The findings in their paper have become part of a lively academic and public debate on how data is used, featured on 25 June in FiveThirtyEight, the popular news website focusing on data-driven news and analysis.
For democracy to function effectively, political parties must offer clear choices to voters during election campaigns. However, as parties’ communication with voters has become increasingly fragmented and targeted, it is much harder for citizens to keep track of what parties are promising. This threatens the quality of democratic representation. It also challenges established research methods for studying parties’ campaign promises. This project will develop new methods for studying parties’ promises in modern election campaigns. The project will integrate existing qualitative methods and develop new research tools based on the Artificial Intelligence (AI) subfields of machine learning and natural language processing. These AI-powered tools will enable researchers to examine parties’ campaign promises in large amounts of text and speech. The resulting research will be of significant benefit to citizens, who will receive greater clarity on the choices that parties are offering. These existing and new methods are highly relevant to research on text and speech in a wide range of social science fields. Until now, progress in this field has been stifled by limited dialogue among the proponents of different qualitative and quantitative methods. The project includes established experts on parties’ campaign promises, new media, qualitative and quantitative methods for analysing political texts, and machine learning and natural language processing.