Artificial Intelligence (AI) is increasingly used for decision-making support in organizations, and especially during the recruitment process. Consequently, recruiters may sometimes find themselves having to process different sources of information (human vs. algorithmic decision support system, ADSS) before deciding to preselect an applicant. Our study aims to explore the mechanisms that lead recruiters to follow or not the recommendations made by human and non-human experts, in particular when they receive contradictory or inaccurate information from these sources. Drawing on results obtained in the field of automated decision support, we make a first general hypothesis that recruiters trust human experts more than ADSS and rely more on their recommendations. Secondly, based on the Judge Advisor System Paradigm (Sniezek & Buckley, 1995), we make a second general hypothesis that the accuracy of the recommendations provided by the dual source of advice influences in different ways the accuracy of recruiters’ preselection decisions. We conducted an experiment involving the screening of resumes by a sample of professionals (N=746) responsible for screening job applications in their work. As hypothesized, the recommendations made to recruiters do influence the accuracy of their decisions. Our results suggest that recruiters comply more with ADSS than human recommendations even if they declare a higher level of trust in human experts. Finally, implications for research and HR policies are discussed.