  Despite the impressive performance of Artificial Intelligence (AI) systems,
their robustness remains elusive and constitutes a key issue that impedes
large-scale adoption. Robustness has been studied in many domains of AI, yet
with different interpretations across domains and contexts. In this work, we
systematically survey the recent progress to provide a reconciled terminology
of concepts around AI robustness. We introduce three taxonomies to organize and
describe the literature both from a fundamental and applied point of view: 1)
robustness by methods and approaches in different phases of the machine
learning pipeline; 2) robustness for specific model architectures, tasks, and
systems; and in addition, 3) robustness assessment methodologies and insights,
particularly the trade-offs with other trustworthiness properties. Finally, we
identify and discuss research gaps and opportunities and give an outlook on the
field. We highlight the central role of humans in evaluating and enhancing AI
robustness, considering the necessary knowledge humans can provide, and discuss
the need for better understanding practices and developing supportive tools in
the future.
