Stanford University, Stanford, CA, USA.
University of California, Berkeley, Berkeley, CA, USA.
Sci Adv. 2020 Feb 14;6(7):eaaz0652. doi: 10.1126/sciadv.aaz0652. eCollection 2020 Feb.
Dressel and Farid recently found that laypeople were as accurate as statistical algorithms in predicting whether a defendant would reoffend, casting doubt on the value of risk assessment tools in the criminal justice system. We report the results of a replication and extension of Dressel and Farid's experiment. Under conditions similar to the original study, we found nearly identical results, with humans and algorithms performing comparably. However, algorithms beat humans in the three other datasets we examined. The performance gap between humans and algorithms was particularly pronounced when, in a departure from the original study, participants were not provided with immediate feedback on the accuracy of their responses. Algorithms also outperformed humans when the information provided for predictions included an enriched (versus restricted) set of risk factors. These results suggest that algorithms can outperform human predictions of recidivism in ecologically valid settings.
德莱塞尔和法里德最近发现，外行人在预测被告是否会再次犯罪方面与统计算法一样准确，这对刑事司法系统中风险评估工具的价值提出了质疑。我们报告了德莱塞尔和法里德实验的复制和扩展结果。在与原始研究相似的条件下，我们发现了几乎相同的结果，人类和算法的表现相当。然而，在我们研究的其他三个数据集，算法的表现优于人类。当参与者没有得到关于他们的反应准确性的即时反馈时，与原始研究不同，人类和算法之间的表现差距尤为明显。当用于预测的信息包括一组丰富（而不是受限）的风险因素时，算法也优于人类。这些结果表明，在生态有效的环境中，算法可以胜过人类对累犯的预测。
