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

Cynthia Rudin () and Berk Ustun ()
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Cynthia Rudin: Departments of Computer Science, Electrical and Computer Engineering, and Statistical Science, Duke University, Durham, North Carolina 27708
Berk Ustun: Center for Research in Computation for Society, Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138

Interfaces, 2018, vol. 48, issue 5, 449-466

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.

Keywords: machine learning; sparse linear models; scoring systems; trust; transparency; interpretability; healthcare; criminal justice; recidivism (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (2)

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