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Optimized Scoring Systems: Toward Trust in Machine Learning for Healthcare and Criminal Justice

Cynthia Rudin & Berk Ustun

INFORMS Journal on Applied Analytics2018https://doi.org/10.1287/inte.2018.0957article
AJG 2ABDC B
Weight
0.73

Abstract

The authors developed and implemented transparent machine-learning models that call into question the use of black-box machine-learning models in healthcare and criminal justice applications.

117 citations

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https://doi.org/https://doi.org/10.1287/inte.2018.0957

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@article{cynthia2018,
  title        = {{Optimized Scoring Systems: Toward Trust in Machine Learning for Healthcare and Criminal Justice}},
  author       = {Cynthia Rudin & Berk Ustun},
  journal      = {INFORMS Journal on Applied Analytics},
  year         = {2018},
  doi          = {https://doi.org/https://doi.org/10.1287/inte.2018.0957},
}

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Optimized Scoring Systems: Toward Trust in Machine Learning for Healthcare and Criminal Justice

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Evidence weight

0.73

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.97 × 0.4 = 0.39
M · momentum0.80 × 0.15 = 0.12
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.