Algorithmic Risk Assessment in the Hands of Humans
Algorithmic Risk Assessment in the Hands of Humans
复制标题
人类手中的算法风险评估
DOI：
10.2139/ssrn.3489440
复制
发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Jennifer L. Doleac
中科院分区：
文献类型：
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作者：
M. Stevenson;Jennifer L. Doleac
我们评估了采用算法预测未来犯罪（风险评估）作为重罪判决中司法裁量权的辅助手段的影响。我们发现，法官的决定受到风险评分的影响，导致得分较高的被告刑期较长，得分较低的被告刑期较短。然而，我们没有发现强有力的证据表明，这种改组导致累犯率下降，随着时间的推移，法官似乎使用的风险scores.Risk评估的失败，以减少累犯至少部分地解释了司法自由裁量权在其使用。尽管年轻被告有很高的再次犯罪风险，但法官仍系统地给予他们宽大处理。这符合长期以来的做法，即在判刑时将青年视为减轻处罚者，因为他们被认为有罪的程度较低。这样的目标冲突可能导致先前的研究高估了法官预测错误的程度。由于风险评分的最重要输入之一实际上是禁区，因此风险评估的预期收益被削减。我们没有发现证据表明风险评估影响全州的种族差异，尽管在使用风险评估最多的法庭上，黑人被告的刑期相对增加。我们进行模拟，以评估如果法官完全遵守与算法相关的量刑建议，种族和年龄差异将如何改变。种族差异可能略有增加，但最大的变化是23岁以下被告的相对监禁率较高。在关于算法的有争议的公开讨论的背景下，我们的研究结果突出了思考人与机器如何互动的重要性。
We evaluate the impacts of adopting algorithmic predictions of future offending (risk assessments) as an aid to judicial discretion in felony sentencing. We find that judges' decisions are influenced by the risk score, leading to longer sentences for defendants with higher scores and shorter sentences for those with lower scores. However, we find no robust evidence that this reshuffling led to a decline in recidivism, and, over time, judges appeared to use the risk scores less. Risk assessment's failure to reduce recidivism is at least partially explained by judicial discretion in its use. Judges systematically grant leniency to young defendants, despite their high risk of reoffending. This is in line with a long standing practice of treating youth as a mitigator in sentencing, due to lower perceived culpability. Such a conflict in goals may have led prior studies to overestimate the extent to which judges make prediction errors. Since one of the most important inputs to the risk score is effectively off-limits, risk assessment's expected benefits are curtailed. We find no evidence that risk assessment affected racial disparities statewide, although there was a relative increase in sentences for black defendants in courts that appeared to use risk assessment most. We conduct simulations to evaluate how race and age disparities would have changed if judges had fully complied with the sentencing recommendations associated with the algorithm. Racial disparities might have increased slightly, but the largest change would have been higher relative incarceration rates for defendants under the age of 23. In the context of contentious public discussions about algorithms, our results highlight the importance of thinking about how man and machine interact.