Machine learning for science and society
Name
10994_2013_5425_ReferencePDF.pdf
Size
213.36 KB
Format
Adobe PDF
Checksum (MD5)
022b7a3302b16686370681d4ba11b6e6
Author(s)
Rudin, Cynthia
Wagstaff, Kiri L.
Date Issued
November 2013
Journal
Machine Learning
Publisher
Springer US
Citation
Rudin, Cynthia, and Kiri L. Wagstaff. “Machine Learning for Science and Society.” Machine Learning 95.1 (2014): 1–9.
Version
Author's final manuscript
Abstract
The special issue on “Machine Learning for Science and Society” showcases machine learning work with influence on our current and future society. These papers address several key problems such as how we perform repairs on critical infrastructure, how we predict severe weather and aviation turbulence, how we conduct tax audits, whether we can detect privacy breaches in access to healthcare data, and how we link individuals across census data sets for new insights into population changes. In this introduction, we discuss the need for such a special issue within the context of our field and its relationship to the broader world. In the era of “big data,” there is a need for machine learning to address important large-scale applied problems, yet it is difficult to find top venues in machine learning where such work is encouraged. We discuss the ramifications of this contradictory situation and encourage further discussion on the best strategy that we as a field may adopt. We also summarize key lessons learned from individual papers in the special issue so that the community as a whole can benefit.
MIT Department
Sloan School of Management
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1007/s10994-013-5425-9
