  Comparing model performances on benchmark datasets is an integral part of
measuring and driving progress in artificial intelligence. A model's
performance on a benchmark dataset is commonly assessed based on a single or a
small set of performance metrics. While this enables quick comparisons, it may
entail the risk of inadequately reflecting model performance if the metric does
not sufficiently cover all performance characteristics. It is unknown to what
extent this might impact benchmarking efforts.
  To address this question, we analysed the current landscape of performance
metrics based on data covering 3867 machine learning model performance results
from the open repository 'Papers with Code'. Our results suggest that the large
majority of metrics currently used have properties that may result in an
inadequate reflection of a models' performance. While alternative metrics that
address problematic properties have been proposed, they are currently rarely
used.
  Furthermore, we describe ambiguities in reported metrics, which may lead to
difficulties in interpreting and comparing model performances.
