论文标题
重复使用刑事风险评估中的反馈效果
Feedback Effects in Repeat-Use Criminal Risk Assessments
论文作者
论文摘要
在刑事法律背景下，风险评估算法被吹捧为数据驱动的，经过良好测试的工具。从业人员通常会引用称为验证测试的研究，以表明一种特定的风险评估算法具有预测的准确性，在风险群体之间建立了合理的差异，并保持了治疗中群体公平的一定程度。为了建立这些重要目标，大多数测试都使用单次单点测量。使用polya urn模型，我们探讨了反馈效应在顺序评分决定过程中的影响。我们通过模拟表明，风险可以以未通过一声测试捕获的方式来传播顺序决策。例如，即使是非常小或无法检测到的风险分配偏差水平也可以放大基于风险的决策，从而导致许多决策迭代后观察到的群体差异。风险评估工具在高度复杂和路径的过程中运行，充满了历史不平等。我们从这项研究中得出结论，这些工具无法正确解释复合效果，需要新的开发和审计方法。
In the criminal legal context, risk assessment algorithms are touted as data-driven, well-tested tools. Studies known as validation tests are typically cited by practitioners to show that a particular risk assessment algorithm has predictive accuracy, establishes legitimate differences between risk groups, and maintains some measure of group fairness in treatment. To establish these important goals, most tests use a one-shot, single-point measurement. Using a Polya Urn model, we explore the implication of feedback effects in sequential scoring-decision processes. We show through simulation that risk can propagate over sequential decisions in ways that are not captured by one-shot tests. For example, even a very small or undetectable level of bias in risk allocation can amplify over sequential risk-based decisions, leading to observable group differences after a number of decision iterations. Risk assessment tools operate in a highly complex and path-dependent process, fraught with historical inequity. We conclude from this study that these tools do not properly account for compounding effects, and require new approaches to development and auditing.
