  Machine learning currently exerts an outsized influence on the world,
increasingly affecting institutional practices and impacted communities. It is
therefore critical that we question vague conceptions of the field as
value-neutral or universally beneficial, and investigate what specific values
the field is advancing. In this paper, we first introduce a method and
annotation scheme for studying the values encoded in documents such as research
papers. Applying the scheme, we analyze 100 highly cited machine learning
papers published at premier machine learning conferences, ICML and NeurIPS. We
annotate key features of papers which reveal their values: their justification
for their choice of project, which attributes of their project they uplift,
their consideration of potential negative consequences, and their institutional
affiliations and funding sources. We find that few of the papers justify how
their project connects to a societal need (15\%) and far fewer discuss negative
potential (1\%). Through line-by-line content analysis, we identify 59 values
that are uplifted in ML research, and, of these, we find that the papers most
frequently justify and assess themselves based on Performance, Generalization,
Quantitative evidence, Efficiency, Building on past work, and Novelty. We
present extensive textual evidence and identify key themes in the definitions
and operationalization of these values. Notably, we find systematic textual
evidence that these top values are being defined and applied with assumptions
and implications generally supporting the centralization of power.Finally, we
find increasingly close ties between these highly cited papers and tech
companies and elite universities.
