Machine Learning that Matters

TL;DR

This work presents six Impact Challenges to explicitly focus the field of machine learning's energy and attention, and discusses existing obstacles that must be addressed.

Abstract

Much of current machine learning (ML) research has lost its connection to problems of import to the larger world of science and society. From this perspective, there exist glaring limitations in the data sets we investigate, the metrics we employ for evaluation, and the degree to which results are communicated back to their originating domains. What changes are needed to how we conduct research to increase the impact that ML has? We present six Impact Challenges to explicitly focus the field?s energy and attention, and we discuss existing obstacles that must be addressed. We aim to inspire ongoing discussion and focus on ML that matters.

Figures (1)

• Figure 1: Three stages of a machine learning research program. Current publishing incentives are highly biased towards the middle row only.