  In response to calls for greater interdisciplinary involvement from the
social sciences and humanities in the development, governance, and study of
artificial intelligence systems, this paper presents one sociologist's view on
the problem of algorithmic bias and the reproduction of societal bias.
Discussions of bias in AI cover much of the same conceptual terrain that
sociologists studying inequality have long understood using more specific terms
and theories. Concerns over reproducing societal bias should be informed by an
understanding of the ways that inequality is continually reproduced in society
-- processes that AI systems are either complicit in, or can be designed to
disrupt and counter. The contrast presented here is between conservative and
radical approaches to AI, with conservatism referring to dominant tendencies
that reproduce and strengthen the status quo, while radical approaches work to
disrupt systemic forms of inequality. The limitations of conservative
approaches to class, gender, and racial bias are discussed as specific
examples, along with the social structures and processes that biases in these
areas are linked to. Societal issues can no longer be out of scope for AI and
machine learning, given the impact of these systems on human lives. This
requires engagement with a growing body of critical AI scholarship that goes
beyond biased data to analyze structured ways of perpetuating inequality,
opening up the possibility for radical alternatives.
