Pursuing Open-Source Development of Predictive Algorithms: The Case of
Criminal Sentencing Algorithms
- URL: http://arxiv.org/abs/2011.06422v1
- Date: Thu, 12 Nov 2020 14:53:43 GMT
- Title: Pursuing Open-Source Development of Predictive Algorithms: The Case of
Criminal Sentencing Algorithms
- Authors: Philip D. Waggoner, Alec Macmillen
- Abstract summary: We argue that open-source algorithm development should be the standard in highly consequential contexts.
We suggest these issues are exacerbated by the proprietary and expensive nature of virtually all widely used criminal sentencing algorithms.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Currently, there is uncertainty surrounding the merits of open-source versus
proprietary algorithm development. Though justification in favor of each
exists, we argue that open-source algorithm development should be the standard
in highly consequential contexts that affect people's lives for reasons of
transparency and collaboration, which contribute to greater predictive accuracy
and enjoy the additional advantage of cost-effectiveness. To make this case, we
focus on criminal sentencing algorithms, as criminal sentencing is highly
consequential, and impacts society and individual people. Further, the
popularity of this topic has surged in the wake of recent studies uncovering
racial bias in proprietary sentencing algorithms among other issues of
over-fitting and model complexity. We suggest these issues are exacerbated by
the proprietary and expensive nature of virtually all widely used criminal
sentencing algorithms. Upon replicating a major algorithm using real criminal
profiles, we fit three penalized regressions and demonstrate an increase in
predictive power of these open-source and relatively computationally
inexpensive options. The result is a data-driven suggestion that if judges who
are making sentencing decisions want to craft appropriate sentences based on a
high degree of accuracy and at low costs, then they should be pursuing
open-source options.
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