  Deep learning has triggered the current rise of artificial intelligence and
is the workhorse of today's machine intelligence. Numerous success stories have
rapidly spread all over science, industry and society, but its limitations have
only recently come into focus. In this perspective we seek to distill how many
of deep learning's problems can be seen as different symptoms of the same
underlying problem: shortcut learning. Shortcuts are decision rules that
perform well on standard benchmarks but fail to transfer to more challenging
testing conditions, such as real-world scenarios. Related issues are known in
Comparative Psychology, Education and Linguistics, suggesting that shortcut
learning may be a common characteristic of learning systems, biological and
artificial alike. Based on these observations, we develop a set of
recommendations for model interpretation and benchmarking, highlighting recent
advances in machine learning to improve robustness and transferability from the
lab to real-world applications.
