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Machine learning for science and society

Publication ,  Journal Article
Rudin, C; Wagstaff, KL
Published in: Machine Learning
January 1, 2014

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. © 2013 The Author(s).

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Published In

Machine Learning

DOI

EISSN

1573-0565

ISSN

0885-6125

Publication Date

January 1, 2014

Volume

95

Issue

1

Start / End Page

1 / 9

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 4611 Machine learning
  • 1702 Cognitive Sciences
  • 0806 Information Systems
  • 0801 Artificial Intelligence and Image Processing
 

Citation

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Rudin, C., & Wagstaff, K. L. (2014). Machine learning for science and society. Machine Learning, 95(1), 1–9. https://doi.org/10.1007/s10994-013-5425-9
Rudin, C., and K. L. Wagstaff. “Machine learning for science and society.” Machine Learning 95, no. 1 (January 1, 2014): 1–9. https://doi.org/10.1007/s10994-013-5425-9.
Rudin C, Wagstaff KL. Machine learning for science and society. Machine Learning. 2014 Jan 1;95(1):1–9.
Rudin, C., and K. L. Wagstaff. “Machine learning for science and society.” Machine Learning, vol. 95, no. 1, Jan. 2014, pp. 1–9. Scopus, doi:10.1007/s10994-013-5425-9.
Rudin C, Wagstaff KL. Machine learning for science and society. Machine Learning. 2014 Jan 1;95(1):1–9.
Journal cover image

Published In

Machine Learning

DOI

EISSN

1573-0565

ISSN

0885-6125

Publication Date

January 1, 2014

Volume

95

Issue

1

Start / End Page

1 / 9

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 4611 Machine learning
  • 1702 Cognitive Sciences
  • 0806 Information Systems
  • 0801 Artificial Intelligence and Image Processing