An algorithm effects a causal representation of relations between features and labels in the human's perception. Such a representation might conflict with the human's prior belief. Explanations can direct the human's attention to the conflicting feature and away from other relevant features. This leads to causal overattribution and may adversely affect the human's information processing. In a field experiment we implemented an XGBoost-trained model as a decision-making aid for counselors at a public employment service to predict candidates' risk of long-term unemployment. The treatment group of counselors was also provided with SHAP. The results show that the quality of the human's decision-making is worse when a feature on which the human holds a conflicting prior belief is displayed as part of the explanation.

翻译：算法在人类的感知中实现了特征与标签之间关系的因果表征。这种表征可能与人类的先验信念相冲突。解释可以将人类的注意力引向冲突特征，同时偏离其他相关特征。这会导致因果过度归因，并可能对人类的认知加工产生不利影响。在一项现场实验中，我们部署了一个XGBoost训练模型，作为公共就业服务机构顾问的决策辅助工具，用于预测求职者长期失业的风险。实验组顾问还获得了SHAP解释工具。结果表明，当解释中显示人类持有冲突先验信念的特征时，其决策质量会变得更差。