English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The paradigm of pretrained deep learning models has recently emerged inartificial intelligence practice, allowing deployment in numerous societalsettings with limited computational resources, but also embedding biases andenabling unintended negative uses. In this paper, we treat pretrained models asobjects of study and discuss the ethical impacts of their sociologicalposition. We discuss how pretrained models are developed and compared under thecommon task framework, but that this may make self-regulation inadequate.Further how pretrained models may have a performative effect on society thatexacerbates biases. We then discuss how pretrained models move through actornetworks as a kind of computationally immutable mobile, but that users also actas agents of technological change by reinterpreting them via fine-tuning andtransfer. We further discuss how users may use pretrained models in maliciousways, drawing a novel connection between the responsible innovation anduser-centered innovation literatures. We close by discussing how thissociological understanding of pretrained models can inform AI governanceframeworks for fairness, accountability, and transparency.

Pretrained AI Models: Performativity, Mobility, and Change