AI identifies corruption risks in the public sector

WU researchers analyse public servants’ susceptibility to corruption worldwide using AI. The results show: individual attitudes towards democracy, competition, and leadership are more relevant than education or income.

Values and attitudes are key

Research on the causes of corruption has so far largely focused on institutional, demographic, and cultural determinants as well as differences between countries. However, the role of individual attitudes and beliefs in shaping public servants’ susceptibility to corruption has received little systematic attention. WU researchers Moritz Schmid and Jurgen Willems have now used artificial intelligence to examine the extent to which corruption susceptibility can be predicted based on individual attitudes and which factors play a key role. “Our results challenge a common assumption: it is not income or education that best predict corruption susceptibility among public servants, but their values and attitudes,” says Moritz Schmid from the Institute for Public Management & Governance. The findings clearly show that a strong orientation towards democratic values tends to be associated with lower corruption susceptibility. “Individuals with a strong commitment to democratic values show less tolerance towards corrupt behaviour,” Schmid explains.