Applications of Generative AI Systems in Social Research

Advancements in Artificial Intelligence (AI) such as Large Language Models (LLM) promise to open unprecedented possibilities in applications with the potential to fundamentally change social research methods. Simultaneously, its development is currently dominated by venture-backed 'BigTech' companies, and it's once again a winner-take-all race. The scientific community is mostly only consumer and not driver of changes, especially from the domains of social science.
These developments do not go without critique. A key concern with AI for social scientists is how to evade methodological black boxes and resist the illusion of Emily Bender's stochastic parrots with their potential risks of hidden biases. The dangers of systems that are not understood sufficiently and that potentially breach privacy, data protection and research ethics principles are crucial.

Some scholars suggest, therefore, that this might be avoidable by gaining a profound understanding of the AI systems' training models and data. However, our understanding of LLM and their training data, for instance, is often limited by proprietary information and business secrets. Hence, we can almost only trust companies with what happens to our data.
Questions on the effectiveness of new laws on the digital frontier (e.g. EU AI Act) to make the principles within these programs understandable by obliging transparency arise, as well as the difficulties of grasping (arithmetic) biases and inequalities, despite their well-documented existence.

Discussing awareness of (arithmetic) biases questions knowledge and resources. Who can really understand LLM? Where do sociologists find resources and time for such an endeavour on top of their research? Should sociology trust computer science with this issue to bridge epistemological differences with another discipline?

This presentation will cover potentials and challenges of AI in societal analysis, especially with qualitative methods, questions of trust and knowledge and will explore possible solutions for feasible sociological AI application.

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