  Deep neural networks (DNNs) often rely on easy-to-learn discriminatory
features, or cues, that are not necessarily essential to the problem at hand.
For example, ducks in an image may be recognized based on their typical
background scenery, such as lakes or streams. This phenomenon, also known as
shortcut learning, is emerging as a key limitation of the current generation of
machine learning models. In this work, we introduce a set of experiments to
deepen our understanding of shortcut learning and its implications. We design a
training setup with several shortcut cues, named WCST-ML, where each cue is
equally conducive to the visual recognition problem at hand. Even under equal
opportunities, we observe that (1) certain cues are preferred to others, (2)
solutions biased to the easy-to-learn cues tend to converge to relatively flat
minima on the loss surface, and (3) the solutions focusing on those preferred
cues are far more abundant in the parameter space. We explain the abundance of
certain cues via their Kolmogorov (descriptional) complexity: solutions
corresponding to Kolmogorov-simple cues are abundant in the parameter space and
are thus preferred by DNNs. Our studies are based on the synthetic dataset
DSprites and the face dataset UTKFace. In our WCST-ML, we observe that the
inborn bias of models leans toward simple cues, such as color and ethnicity.
Our findings emphasize the importance of active human intervention to remove
the inborn model biases that may cause negative societal impacts.
