Algorithms as Co-Researchers. Exploring Meaning and Bias in Qualitative Research

Abstract

This chapter discusses the reflexive relationship between qualitative researchers and the process of selecting, forming, processing and interpreting data in algorithmic qualitative research. Drawing on Heidegger’s ideas, it argues that such research is necessarily synthetic – even creative – in that these activities inflect, and are in turn inflected by, the data itself. Thus, methodological transparency is key to understanding how different types of meanings become infused in the process of algorithmic qualitative research. While algorithmic research practices provide multiple opportunities for creating transparent meaning, researchers are urged to consider how such practices can also introduce and reinforce human and algorithmic bias in the form of unacknowledged introduction of perspectives into the data. The chapter demonstrates this reflexive dance of meaning and bias using an illustrative case of topic modelling. It closes by offering some recommendations for engaging actively with the domain, considering a multi-disciplinary approach, and adopting complementary methods that could potentially help researchers in fostering transparency and meaning.