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

TY - JOUR

T1 - No Thanks, Dear AI!

T2 - Understanding the Effects of Disclosure and Deployment of Artificial Intelligence in Public Sector Recruitment

AU - Keppeler, Florian

PY - 2024/1

Y1 - 2024/1

N2 - 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 linear regression 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, this study provides a theoretical and empirical disentanglement of the disclosure effect and the deployment effect to inform future evaluations 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 recruitment.

AB - 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 linear regression 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, this study provides a theoretical and empirical disentanglement of the disclosure effect and the deployment effect to inform future evaluations 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 recruitment.

KW - Algorithmic decision-making

KW - Algorithm aversion

KW - Digital discretion

KW - Field experiment

KW - Employer-driven recruitment

UR - http://www.scopus.com/inward/record.url?scp=85174633365&partnerID=8YFLogxK

U2 - 10.1093/jopart/muad009

DO - 10.1093/jopart/muad009

M3 - Journal article

SN - 1053-1858

VL - 34

SP - 39

EP - 52

JO - Journal of Public Administration Research and Theory

JF - Journal of Public Administration Research and Theory

IS - 1

ER -