University of Bonn, Germany.
Child Abuse Negl. 2024 Aug;154:106943. doi: 10.1016/j.chiabu.2024.106943. Epub 2024 Jul 16.
Child welfare agencies around the world have experimented with algorithmic predictive modeling as a method to assist in decision making regarding foster child risk, removal and placement.
Thus far, all of the predictive risk models have been confined to the employees of the various child welfare agencies at the early removal stages and none have been used by attorneys in legal arguments or by judges in making child welfare legal decisions. This study will show the effects of a predictive model on legal decision making within a child welfare context.
Lawyers, judges and law students with experience in child welfare or juvenile law were recruited to take an online randomized vignette survey.
The survey consisted of two vignettes describing complex foster child removal and placement legal decisions where participants were exposed to one of three randomized predictive risk model scores. They were then asked follow up questions regarding their decisions to see if the risk models changed their answers.
Using structural equation modeling, high predictive model risk scores showed consistent ability to change legal decisions about removal and placement across both vignettes. Medium and low scores, though less consistent, also significantly influenced legal decision making.
Child welfare legal decision making can be affected by the use of a predictive risk model, which has implications for the development and use of these models as well as legal education for attorneys and judges in the field.
世界各地的儿童福利机构都尝试使用算法预测模型来协助决策，以确定寄养儿童的风险、是否需要将其带走以及安置何处。
迄今为止，所有的预测风险模型都局限于各儿童福利机构员工在早期移除阶段使用，而没有律师在法律辩论中或法官在做出儿童福利法律决策时使用。本研究将展示预测模型在儿童福利背景下对法律决策的影响。
招募了有儿童福利或少年法经验的律师、法官和法律系学生参加在线随机案例调查。
调查包括两个描述复杂寄养儿童移除和安置法律决策的案例，参与者接触到三种随机预测风险模型评分中的一种。然后，他们被问到后续问题，以了解风险模型是否改变了他们的答案。
使用结构方程模型，高预测模型风险评分在两个案例中都显示出了改变移除和安置相关法律决策的一致能力。中低评分虽然不太一致，但也显著影响了法律决策。
儿童福利法律决策可能会受到预测风险模型的使用的影响，这对这些模型的开发和使用以及该领域律师和法官的法律教育都有影响。
