  Two distinct trends can prove the existence of technological unemployment in
the US. First, there are more open jobs than the number of unemployed persons
looking for a job, and second, the shift of the Beveridge curve. There have
been many attempts to find the cause of technological unemployment. However,
all of these approaches fail when it comes to evaluating the impact of modern
technologies on employment future. This study hypothesizes that rather than
looking into skill requirement or routine non-routine discrimination of tasks,
a holistic approach is required to predict which occupations are going to be
vulnerable with the advent of this 4th industrial revolution, i.e., widespread
application of AI, ML algorithms, and Robotics. Three critical attributes are
considered: bottleneck, hazardous, and routine. Forty-five relevant attributes
are chosen from the O*NET database that can define these three types of tasks.
Performing Principal Axis Factor Analysis, and K-medoid clustering, the study
discovers a list of 367 vulnerable occupations. The study further analyzes the
last nine years of national employment data and finds that over the previous
four years, the growth of vulnerable occupations is only half than that of
non-vulnerable ones despite the long rally of economic expansion.
