论文标题
追求预测算法的开源开发：刑事量刑算法的案例
Pursuing Open-Source Development of Predictive Algorithms: The Case of Criminal Sentencing Algorithms
论文作者
论文摘要
目前，开源与专有算法开发的优点存在不确定性。尽管有利于每个存在的理由，但我们认为，出于透明和协作的原因，开源算法的开发应该是影响人们生活的标准，这有助于提高预测准确性，并享受成本效益的额外优势。为了使此案，我们将重点放在刑事判决算法上，因为刑事判决是高度的，并影响了社会和个人。此外，在最近的研究中发现种族偏见在专有量刑算法以及其他过度拟合和模型复杂性的问题之后，该主题的普及迅速增长。我们建议这些问题因几乎所有使用的刑事判决算法的专有性和昂贵的性质而加剧。在使用实际的犯罪分子复制主要算法后，我们符合三个惩罚回归，并证明了这些开源和相对计算的廉价选择的预测能力增加。结果是数据驱动的建议，如果做出判决决定的法官希望根据高度准确性和低成本来制定适当的句子，那么他们应该追求开源选择。
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.
