  The development of state-of-the-art systems in different applied areas of
machine learning (ML) is driven by benchmarks, which have shaped the paradigm
of evaluating generalisation capabilities from multiple perspectives. Although
the paradigm is shifting towards more fine-grained evaluation across diverse
tasks, the delicate question of how to aggregate the performances has received
particular interest in the community. In general, benchmarks follow the
unspoken utilitarian principles, where the systems are ranked based on their
mean average score over task-specific metrics. Such aggregation procedure has
been viewed as a sub-optimal evaluation protocol, which may have created the
illusion of progress. This paper proposes Vote'n'Rank, a framework for ranking
systems in multi-task benchmarks under the principles of the social choice
theory. We demonstrate that our approach can be efficiently utilised to draw
new insights on benchmarking in several ML sub-fields and identify the
best-performing systems in research and development case studies. The
Vote'n'Rank's procedures are more robust than the mean average while being able
to handle missing performance scores and determine conditions under which the
system becomes the winner.
