  Studies conducted on Western, Educated, Industrialized, Rich, and Democratic
(WEIRD) samples are considered atypical of the world's population and may not
accurately represent human behavior. In this study, we aim to quantify the
extent to which the ACM FAccT conference, the leading venue in exploring
Artificial Intelligence (AI) systems' fairness, accountability, and
transparency, relies on WEIRD samples. We collected and analyzed 128 papers
published between 2018 and 2022, accounting for 30.8% of the overall
proceedings published at FAccT in those years (excluding abstracts, tutorials,
and papers without human-subject studies or clear country attribution for the
participants). We found that 84% of the analyzed papers were exclusively based
on participants from Western countries, particularly exclusively from the U.S.
(63%). Only researchers who undertook the effort to collect data about local
participants through interviews or surveys added diversity to an otherwise
U.S.-centric view of science. Therefore, we suggest that researchers collect
data from under-represented populations to obtain an inclusive worldview. To
achieve this goal, scientific communities should champion data collection from
such populations and enforce transparent reporting of data biases.
