  As generative language models are deployed in ever-wider contexts, concerns
about their political values have come to the forefront with critique from all
parts of the political spectrum that the models are biased and lack neutrality.
However, the question of what neutrality is and whether it is desirable remains
underexplored. In this paper, I examine neutrality through an audit of Delphi
[arXiv:2110.07574], a large language model designed for crowdsourced ethics. I
analyse how Delphi responds to politically controversial questions compared to
different US political subgroups. I find that Delphi is poorly calibrated with
respect to confidence and exhibits a significant political skew. Based on these
results, I examine the question of neutrality from a data-feminist lens, in
terms of how notions of neutrality shift power and further marginalise unheard
voices. These findings can hopefully contribute to a more reflexive debate
about the normative questions of alignment and what role we want generative
models to play in society.
