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Optimized scoring systems: Toward trust in machine learning for healthcare and criminal justice

Publication ,  Journal Article
Rudin, C; Ustunb, B
Published in: Interfaces
September 1, 2018

Abstract. Questions of trust in machine-learning models are becoming increasingly important as these tools are starting to be used widely for high-stakes decisions in medicine and criminal justice. Transparency of models is a key aspect affecting trust. This paper reveals that there is new technology to build transparent machine-learning models that are often as accurate as black-box machine-learning models. These methods have already had an impact in medicine and criminal justice. This work calls into question the overall need for black-box models in these applications. Copyright:

Duke Scholars

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Published In

Interfaces

DOI

EISSN

1526-551X

ISSN

0092-2102

Publication Date

September 1, 2018

Volume

48

Issue

5

Start / End Page

449 / 466

Related Subject Headings

  • Operations Research
  • 4901 Applied mathematics
  • 3509 Transportation, logistics and supply chains
  • 1503 Business and Management
  • 0806 Information Systems
  • 0102 Applied Mathematics
 

Citation

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ICMJE
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Rudin, C., & Ustunb, B. (2018). Optimized scoring systems: Toward trust in machine learning for healthcare and criminal justice. Interfaces, 48(5), 449–466. https://doi.org/10.1287/inte.2018.0957
Rudin, C., and B. Ustunb. “Optimized scoring systems: Toward trust in machine learning for healthcare and criminal justice.” Interfaces 48, no. 5 (September 1, 2018): 449–66. https://doi.org/10.1287/inte.2018.0957.
Rudin, C., and B. Ustunb. “Optimized scoring systems: Toward trust in machine learning for healthcare and criminal justice.” Interfaces, vol. 48, no. 5, Sept. 2018, pp. 449–66. Scopus, doi:10.1287/inte.2018.0957.

Published In

Interfaces

DOI

EISSN

1526-551X

ISSN

0092-2102

Publication Date

September 1, 2018

Volume

48

Issue

5

Start / End Page

449 / 466

Related Subject Headings

  • Operations Research
  • 4901 Applied mathematics
  • 3509 Transportation, logistics and supply chains
  • 1503 Business and Management
  • 0806 Information Systems
  • 0102 Applied Mathematics