Reflexive Research with LLMs: Considering the Positionality of Users and Systems

Abstract

Previous work has found that people often perceive computational systems as neutral tools (van Es, 2023), and yet these systems are not developed or deployed within a vacuum. As the popularity of Large Language Models (LLMs) in digital social science and humanities (DSSH) research increases, it is important that we reflect both on our positionality as researchers regarding how we are primed to interact with these systems and the positionality of the systems themselves as defined by their design and training. This paper presents a model of factors and interactions affecting the use of LLMs in DSSH research and argues that explicit discussion of both human biases, which affect how we interact with systems, and the potential biases encoded in systems are needed in conjunction with strong case specific system evaluation when developing methodologically sound applications of LLMs.

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