  We present the first results from Citizen ASAS-SN, a citizen science project
for the All-Sky Automated Survey for Supernovae (ASAS-SN) hosted on the
Zooniverse platform. Citizen ASAS-SN utilizes the newer, deeper, higher cadence
ASAS-SN $g$-band data and tasks volunteers to classify periodic variable star
candidates based on their phased light curves. We started from 40,640 new
variable candidates from an input list of ${\sim} 7.4$ million stars with
$\delta < -60^\circ$ and the volunteers identified 10,420 new discoveries which
they classified as 4,234 pulsating variables, 3,132 rotational variables, 2,923
eclipsing binaries, and 131 variables flagged as Unknown. They classified known
variable stars with an accuracy of 89% for pulsating variables, 81% for
eclipsing binaries, and 49% for rotational variables. We examine user
performance, agreement between users, and compare the citizen science
classifications with our machine learning classifier updated for the $g$-band
light curves. In general, user activity correlates with higher classification
accuracy and higher user agreement. We used the user's "Junk" classifications
to develop an effective machine learning classifier to separate real from false
variables, and there is a clear path for using this "Junk" training set to
significantly improve our primary machine learning classifier. We also
illustrate the value of Citizen ASAS-SN for identifying unusual variables with
several examples.
