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Paper short abstract

The presentation shows the results of a walkthrough-style analysis of 5 AI-based tools for qualitative data analysis, and argues that these tools reconfigure traditional criteria of qualitative analysis, often in non-transparent ways.

Paper long abstract

The presentation shows the results of the critical, walkthrough-style analysis of a diverse sample of AI-based tools for qualitative social data analysis, with a particular focus on their implications for representing the social world.

The development of artificial intelligence (AI), including large language models (LLMs), has often been heralded as profoundly impacting social research. These new technologies have been promoted as not only supporting such research activities but as extensions or even alternatives to traditional social research (Airoldi 2021; Grossman et al. 2023; Boag et al. 2024). However, it can also be argued that public debate remains dominated by simplified, extreme techno-phobic and techno-enthusiastic narratives that make it difficult to see the complex implications of adopting such tools in representing social realities (Dahlin 2022; Schinkel 2023).

The presentation shows the results of our analysis of 5 AI-based tools for qualitative data analysis using the walkthrough method drawing on the STS tradition (Light et al. 2018) and focusing on the applications’ intended use, audience, and embedded social meanings. In our presentation, we will focus on how these apps relate to four fundamental epistemological criteria of qualitative research: credibility, intersubjectivity, reflexivity, and ethics. In our interpretation, the discussed tools can hardly be treated as “innocent”. Instead, they should rather be seen as introducing new meanings and redefining traditional criteria of qualitative analysis, often in non-transparent ways.