Title: Demographics and discussion influence views on algorithmic fairness,
Abstract: The field of algorithmic fairness has highlighted ethical questions which may
not have purely technical answers. For example, different algorithmic fairness
constraints are often impossible to satisfy simultaneously, and choosing
between them requires value judgments about which people may disagree.
Achieving consensus on algorithmic fairness will be difficult unless we
understand why people disagree in the first place. Here we use a series of
surveys to investigate how two factors affect disagreement: demographics and
discussion. First, we study whether disagreement on algorithmic fairness
questions is caused partially by differences in demographic backgrounds. This
is a question of interest because computer science is demographically
non-representative. If beliefs about algorithmic fairness correlate with
demographics, and algorithm designers are demographically non-representative,
decisions made about algorithmic fairness may not reflect the will of the
population as a whole. We show, using surveys of three separate populations,
that there are gender differences in beliefs about algorithmic fairness. For
example, women are less likely to favor including gender as a feature in an
algorithm which recommends courses to students if doing so would make female
students less likely to be recommended science courses. Second, we investigate
whether people's views on algorithmic fairness can be changed by discussion and
show, using longitudinal surveys of students in two computer science classes,
that they can.