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Currently, there is uncertainty surrounding the merits of open-source versusproprietary algorithm development. Though justification in favor of eachexists, we argue that open-source algorithm development should be the standardin highly consequential contexts that affect people's lives for reasons oftransparency and collaboration, which contribute to greater predictive accuracyand enjoy the additional advantage of cost-effectiveness. To make this case, wefocus on criminal sentencing algorithms, as criminal sentencing is highlyconsequential, and impacts society and individual people. Further, thepopularity of this topic has surged in the wake of recent studies uncoveringracial bias in proprietary sentencing algorithms among other issues ofover-fitting and model complexity. We suggest these issues are exacerbated bythe proprietary and expensive nature of virtually all widely used criminalsentencing algorithms. Upon replicating a major algorithm using real criminalprofiles, we fit three penalized regressions and demonstrate an increase inpredictive power of these open-source and relatively computationallyinexpensive options. The result is a data-driven suggestion that if judges whoare making sentencing decisions want to craft appropriate sentences based on ahigh degree of accuracy and at low costs, then they should be pursuingopen-source options.

Pursuing Open-Source Development of Predictive Algorithms: The Case of Criminal Sentencing Algorithms