Intuitions, analytics, and killing ants: Inference literacy of high school-educated adults in the US
Abstract
Analytic systems increasingly allow companies to draw inferences about users’
characteristics, yet users may not fully understand these systems due to their
complex and often unintuitive nature. In this paper, we investigate inference
literacy: the beliefs and misconceptions people have about how companies collect
and make inferences from their data. We interviewed 21 non-student participants
with a high school education, finding that few believed companies can make the type
of deeply personal inferences that companies now routinely make through machine
learning. Instead, most participant’s inference literacy beliefs clustered around
one of two main concepts: one cluster believed companies make inferences about a
person based largely on a priori stereotyping, using directly gathered demographic
data; the other cluster believed that companies make inferences based on computer
processing of online behavioral data, but often expected these inferences to be
limited to straightforward intuitions. We also find evidence that cultural models
related to income and ethnicity influence the assumptions that users make about
their own role in the data economy. We share implications for research, design, and
policy on tech savviness, digital inequality, and potential inference literacy
interventions.
