  The paradigm of pretrained deep learning models has recently emerged in
artificial intelligence practice, allowing deployment in numerous societal
settings with limited computational resources, but also embedding biases and
enabling unintended negative uses. In this paper, we treat pretrained models as
objects of study and discuss the ethical impacts of their sociological
position. We discuss how pretrained models are developed and compared under the
common task framework, but that this may make self-regulation inadequate.
Further how pretrained models may have a performative effect on society that
exacerbates biases. We then discuss how pretrained models move through actor
networks as a kind of computationally immutable mobile, but that users also act
as agents of technological change by reinterpreting them via fine-tuning and
transfer. We further discuss how users may use pretrained models in malicious
ways, drawing a novel connection between the responsible innovation and
user-centered innovation literatures. We close by discussing how this
sociological understanding of pretrained models can inform AI governance
frameworks for fairness, accountability, and transparency.
