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Short abstract

This paper examines epistemic quality, ethical integrity and practical sovereignty as three dimensions for a responsible way of co-operating with AI systems in STS-related research and beyond.

Long abstract

STS-informed research has long engaged critically with the challenging dimensions of digital research methodologies while proposing constructive ways to navigate them (Rogers, 2013). This attentiveness to the performativity of methods is reflected in approaches advocating for inventive methods (Wakeford/Lury, 2014), multifarious instruments (Marres, 2017), and an embrace of methodological messiness (Law, 2004; 2006). At the same time, researchers across disciplines are increasingly experimenting with AI-based tools to investigate matters of concern in science and technology — from technology impact analysis and ethics research to computational social science (Hirsbrunner et al., 2022; Hirsbrunner, 2025).

The integration of generative and agentic AI systems into research practice introduces, however, a distinct set of methodological, ethical and practical challenges. Drawing on our own investigations experimenting with generative and agentic AI elements, we ask how epistemic quality, ethical integrity and practical sovereignty can be cultivated within such research constellations. By epistemic quality, we mean the scientific soundness of AI-assisted inquiry. To what extent must AI-generated outputs be reproducible and epistemically accountable? Which methodologies and insights prevail beyond the current AI hype? By ethical integrity, we engage with the normative challenges of operating alongside AI systems — acknowledging their role as vector of hegemonic knowledge production and their susceptibility to discriminatory bias. By sovereignty, we refer to researchers' capacity to retain meaningful agency over their methods, instruments, and research objects — encompassing technological dependency, contestability, and the interchangeability of sociotechnical elements. We argue that these three dimensions are mutually entangled and demand an integrated analytical approach.