  The step of expert taxa recognition currently slows down the response time of
many bioassessments. Shifting to quicker and cheaper state-of-the-art machine
learning approaches is still met with expert scepticism towards the ability and
logic of machines. In our study, we investigate both the differences in
accuracy and in the identification logic of taxonomic experts and machines. We
propose a systematic approach utilizing deep Convolutional Neural Nets with the
transfer learning paradigm and extensively evaluate it over a multi-pose
taxonomic dataset with hierarchical labels specifically created for this
comparison. We also study the prediction accuracy on different ranks of
taxonomic hierarchy in detail. We used support vector machine classifier as a
benchmark. Our results revealed that human experts using actual specimens yield
the lowest classification error ($\overline{CE}=6.1\%$). However, a much
faster, automated approach using deep Convolutional Neural Nets comes close to
human accuracy ($\overline{CE}=11.4\%$) when a typical flat classification
approach is used. Contrary to previous findings in the literature, we find that
for machines following a typical flat classification approach commonly used in
machine learning performs better than forcing machines to adopt a hierarchical,
local per parent node approach used by human taxonomic experts
($\overline{CE}=13.8\%$). Finally, we publicly share our unique dataset to
serve as a public benchmark dataset in this field.
