Machine Learning that Matters

Machine learning has a very important role to play in human genetics and genetic epidemiology. However, the computer science-based machine learning community has come under fire for writing and publishing papers that lack real-world application and impactful interpretation of results. The following is a must read for anyone interested in developing machine learning methods.

Machine Learning that Matters [PDF]

Kiri L. Wagstaff
kiri.l.wagstaff@jpl.nasa.gov
Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, Pasadena, CA 91109 USA

To appear in Proceedings of the 29 th International Conference on Machine Learning, Edinburgh, Scotland, UK, 2012.

arXiv:1206.4656v1

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.