No Thanks, Dear AI! Understanding the Effects of Disclosure and Deployment of Artificial Intelligence in Public Sector Recruitment

Abstract

Applications based on artificial intelligence (AI) play an increasing role in the public sector and invoke political discussions. Research gaps exist regarding the disclosure effects—reactions to disclosure of the use of AI applications—and the deployment effect—efficiency gains in data savvy tasks. This study analyzes disclosure effects and explores the deployment of an AI application in a pre-registered field experiment (n=2,000) co-designed with a public organization in the context of employer-driven recruitment. The results show that disclosing the use of the AI application leads to significantly less interest in an offer among job candidates. The explorative analysis of the deployment of the AI application indicates that the person–job fit determined by the leaders can be predicted by the AI application. Based on the literature on algorithm aversion and digital discretion, the study offers a theoretical and empirical disentanglement of the disclosure effect and the deployment effect to support the evaluation of AI applications in the public sector. It contributes to the understanding of how AI applications can shape public policy and management decisions, and discusses the potential benefits and downsides of disclosing and deploying AI applications in the public sector and in employer-driven public sector recruitment.

The version of record is available online at: https://doi.org/10.1093/jopart/muad009
A postprint version is available online at: https://florian-keppeler.com/research/

Keywords: Algorithmic decision-making, algorithm aversion, digital discretion, field experiment, employer-driven recruitment