Notes towards infrastructure governance for large language models

Abstract

This paper draws on information infrastructures (IIs) in science and technology studies (STS), as well as on feminist STS scholarship and contemporary critical accounts of digital technologies, to build an initial mapping of the infrastructural mechanisms and implications of large language models (LLMs). Through a comparison with discriminatory machine learning (ML) systems and a case study on gender bias, I present LLMs as contested artefacts with categorising and performative capabilities. This paper suggests that generative systems do not tangibly depart from traditional, discriminative counterparts in terms of their underlying probabilistic mechanisms, and therefore both technologies can be theorised as infrastructures of categorisation. However, LLMs additionally retain performative capabilities through their linguistic outputs. Here, I outline the intuition behind this phenomenon, which I refer to as “language as infrastructure”. While traditional, discriminative systems “disappear” into larger IIs, the hype surrounding generative technologies presents an opportunity to scrutinise these artefacts, to alter their computational mechanisms and introduce governance measures]. I illustrate this thesis through Sharma’s formulation of “broken machine”, and suggest dataset curation and participatory design as governance mechanisms that can partly address downstream harms in LLMs (Barocas, et al., 2023).