  Drawing from the resources of psychoanalysis and critical media studies, in
this paper we develop an analysis of Large Language Models (LLMs) as automated
subjects. We argue the intentional fictional projection of subjectivity onto
LLMs can yield an alternate frame through which AI behaviour, including its
productions of bias and harm, can be analysed. First, we introduce language
models, discuss their significance and risks, and outline our case for
interpreting model design and outputs with support from psychoanalytic
concepts. We trace a brief history of language models, culminating with the
releases, in 2022, of systems that realise state-of-the-art natural language
processing performance. We engage with one such system, OpenAI's InstructGPT,
as a case study, detailing the layers of its construction and conducting
exploratory and semi-structured interviews with chatbots. These interviews
probe the model's moral imperatives to be helpful, truthful and harmless by
design. The model acts, we argue, as the condensation of often competing social
desires, articulated through the internet and harvested into training data,
which must then be regulated and repressed. This foundational structure can
however be redirected via prompting, so that the model comes to identify with,
and transfer, its commitments to the immediate human subject before it. In
turn, these automated productions of language can lead to the human subject
projecting agency upon the model, effecting occasionally further forms of
countertransference. We conclude that critical media methods and psychoanalytic
theory together offer a productive frame for grasping the powerful new
capacities of AI-driven language systems.
