I enjoyed this paper, "The Problem with Metrics is a Fundamental Problem for AI". In ML, often we glamorize algorithmic or model improvements and ignore the importance of what is around that such as the data used, metrics for success, or a good UI for the humans trying to get something done with our models. It is important that ML systems optimize for the right thing, and they often do not because metrics for success are picked too sloppily and hastily.
https://arxiv.org/abs/2002.08512
arXiv.orgThe Problem with Metrics is a Fundamental Problem for AIOptimizing a given metric is a central aspect of most current AI approaches,
yet overemphasizing metrics leads to manipulation, gaming, a myopic focus on
short-term goals, and other unexpected negative consequences. This poses a
fundamental contradiction for AI development. Through a series of real-world
case studies, we look at various aspects of where metrics go wrong in practice
and aspects of how our online environment and current business practices are
exacerbating these failures. Finally, we propose a framework towards mitigating
the harms caused by overemphasis of metrics within AI by: (1) using a slate of
metrics to get a fuller and more nuanced picture, (2) combining metrics with
qualitative accounts, and (3) involving a range of stakeholders, including
those who will be most impacted.
@glinden it's an interesting perspective from econ to relate Goodhart's Law to the machine learning community. and for researchers we need to care more about the true impact of the tasks instead of the metric-based optimization itself.