Ludwig Jonas, Heineck Paul-Michael, Hess Marie-Theres, Kremeti Eleni, Tauschhuber Max, Hilgendorf Eric, Deutsch Roland
Department of Psychology II-Social Psychology, Julius-Maximilians-University Wurzburg.
Department of Criminal Law, Criminal Justice, Legal Theory, Information and Computer Science Law, Julius-Maximilians-University Wurzburg.
Law Hum Behav. 2024 Oct-Dec;48(5-6):441-455. doi: 10.1037/lhb0000577. Epub 2024 Sep 12.
Algorithmic decision making (ADM) takes on increasingly complex tasks in the criminal justice system. Whereas new developments in machine learning could help to improve the quality of judicial decisions, there are legal and ethical concerns that thwart the widespread use of algorithms. Against the backdrop of current efforts to promote the digitization of the German judicial system, this research investigates motivational factors (pragmatic motives, fairness concerns, and self-image-related considerations) that drive or impede the acceptance of ADM in court.
We tested two hypotheses: (1) Perceived threat of inequality in legal judgments increases ADM acceptance, and (2) experts (judges) are more skeptical toward technological innovation than novices (general population).
We conducted a preregistered experiment with 298 participants from the German general population and 267 judges at regional courts in Bavaria to study how inequality threat (vs. control) relates to ADM acceptance in court, usage intentions, and attitudes.
In partial support of the first prediction, inequality threat increased ADM acceptance, effect size = 0.24, 95% confidence interval (CI) [0.01, 0.47], and usage intentions ( = 0.23, 95% CI [0.00, 0.46]) of laypeople. Unexpectedly, however, this was not the case for experts. Moreover, ADM attitudes remained unaffected by the experimental manipulation in both groups. As predicted, judges held more negative attitudes toward ADM than the general population ( = -0.71, 95% CI [-0.88, -0.54]). Exploratory analysis suggested that generalized attitudes emerged as the strongest predictor of judges' intentions to use ADM in their own court proceedings.
These findings elucidate the motivational forces that drive algorithm aversion and acceptance in a criminal justice context and inform the ongoing debate about perceptions of fairness in human-computer interaction. Implications for judicial praxis and the regulation of ADM in the German legal framework are discussed. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
算法决策(ADM)在刑事司法系统中承担着日益复杂的任务。虽然机器学习的新发展有助于提高司法决策的质量,但存在一些法律和伦理问题阻碍了算法的广泛应用。在当前推动德国司法系统数字化的努力背景下,本研究调查了驱动或阻碍法庭接受ADM的动机因素(务实动机、公平担忧和与自我形象相关的考虑因素)。
我们测试了两个假设:(1)法律判决中不平等的感知威胁会增加对ADM的接受度,以及(2)专家(法官)比新手(普通民众)对技术创新更为怀疑。
我们对来自德国普通民众的298名参与者和巴伐利亚州地方法院的267名法官进行了一项预先注册的实验,以研究不平等威胁(与对照组相比)与法庭上对ADM的接受度、使用意图和态度之间的关系。
部分支持第一个预测,不平等威胁增加了普通民众对ADM的接受度,效应量=0.24,95%置信区间(CI)[0.01,0.47],以及使用意图(=0.23,95%CI[0.00,0.46])。然而,出乎意料的是,专家并非如此。此外,两组中ADM态度均未受实验操作影响。如预测的那样,法官对ADM的态度比普通民众更为消极(=-0.71,95%CI[-0.88,-0.54])。探索性分析表明,总体态度是法官在自己的法庭程序中使用ADM意图的最强预测因素。
这些发现阐明了在刑事司法背景下驱动算法厌恶和接受的动机力量,并为正在进行的关于人机交互中公平感的辩论提供了信息。讨论了对德国法律框架下司法实践和ADM监管的启示。(PsycInfo数据库记录(c)2025美国心理学会,保留所有权利)