Fairness perceptions of AI use by tax administration

We tested whether the proportion of AI versus auditors in fraud selection matters for fairness, and whether there is an impact of transparency (explanations). We found that a higher proportion of AI was more procedurally fair, mostly through bias suppression and consistency, and that the attitude toward AI and trust in the administration explained most variance. Transparency (explanations) had no impact. We also found two small negative interaction effects concerning trust and procedural fairness: with high trust in the tax administration, fairness increased less (as AI increased). Conversely, with low trust, fairness increased more (as AI increased). Dataset 1 was used for the pilot (with students and professionals) Dataset 2 was a representative dataset for the Flemish population.

本研究旨在探讨在欺诈选择过程中，AI与审计人员比例的分配对公平性的影响，以及透明度（解释性）是否会产生影响。研究发现，AI比例较高时，在程序公平性方面表现更为优越，这主要得益于偏差抑制和一致性提升，并且对AI的态度以及对管理层的信任解释了大部分的方差。透明度（解释性）并未产生显著影响。此外，我们还发现信任与程序公平性之间存在两种微小的负向交互效应：在对税务机关高度信任的情况下，随着AI比例的增加，公平性的提升程度较低；反之，在信任度较低的情况下，随着AI比例的增加，公平性的提升程度较高。数据集1用于试点研究（涉及学生和专业人士），数据集2则是弗拉芒人口的代表性数据集。