Should I Trust the Artificial Intelligence to Recruit? Recruiters’ Perceptions and Behavior When Faced With Algorithm-Based Recommendation Systems During Resume Screening

Abstract

Resume screening assisted by decision support systems that incorporate artificial intelligence is currently undergoing a strong development in many organizations, raising technical, managerial, legal, and ethical issues. The purpose of the present paper is to better understand the reactions of recruiters when they are offered algorithm-based recommendations during resume screening. Two polarized attitudes have been identified in the literature on users’ reactions to algorithm-based recommendations: algorithm aversion, which reflects a general distrust and preference for human recommendations; and automation bias, which corresponds to an overconfidence in the decisions or recommendations made by algorithmic decision support systems (ADSS). Drawing on results obtained in the field of automated decision support areas, we make the general hypothesis that recruiters trust human experts more than ADSS, because they distrust algorithms for subjective decisions such as recruitment. An experiment on resume screening was conducted on a sample of professionals (N = 694) involved in the screening of job applications. They were asked to study a job offer, then evaluate two fictitious resumes in a 2 × 2 factorial design with manipulation of the type of recommendation (no recommendation/algorithmic recommendation/human expert recommendation) and of the consistency of the recommendations (consistent vs. inconsistent recommendation). Our results support the general hypothesis of preference for human recommendations: recruiters exhibit a higher level of trust toward human expert recommendations compared with algorithmic recommendations. However, we also found that recommendation’s consistence has a differential and unexpected impact on decisions: in the presence of an inconsistent algorithmic recommendation, recruiters favored the unsuitable over the suitable resume. Our results also show that specific personality traits (extraversion, neuroticism, and self-confidence) are associated with a differential use of algorithmic recommendations. Implications for research and HR policies are finally discussed.

Exploring the Ethical Dynamics of the Use of Artificial Intelligence (AI) in Hiring in Healthcare Organizations

“…When not designed with fairness as a priority, AI algorithms may inadvertently perpetuate systemic biases prevalent in historical hiring decisions. This can lead to the exclusion of qualified candidates from diverse backgrounds, undermining the principle of equal opportunities and potentially reinforcing existing inequalities (Deshpande et al, 2020;Mujtaba & Mahapatra, 2019;Lacroux & Martin-Lacroux, 2022).…”

“…Lack of transparency in AI algorithms can hinder the ability to understand and challenge the decisions made during the screening process. Organizations must establish clear guidelines and accountability mechanisms to ensure that candidates have a fair chance to understand and contest the outcomes (Deshpande et al, 2020;Mujtaba & Mahapatra, 2019;Lacroux & Martin-Lacroux, 2022).…”

“…AI systems rely on historical data to learn patterns and make decisions. Biases present in the training data, such as the underrepresentation of specific demographics or industries, can perpetuate discriminatory hiring practices (Deshpande et al, 2020;Mujtaba & Mahapatra, 2019;Lacroux & Martin-Lacroux, 2022).…”

Prevalence of bias against neurodivergence‐related terms in artificial intelligence language models

“…Humans are increasingly relying more and more on artificial intelligence (AI). AI plays a key role in tasks ranging from medical decision‐making (Giordano et al, 2021; Kumar et al, 2022) to resume scanning and job candidate selection (Lacroux & Martin‐Lacroux, 2022) to dating apps (Sharabi, 2022; Wu & Kelly, 2020).…”

Humans inherit artificial intelligence biases

“…Over the last decades, the number of tools based on artificial intelligence (AI) designed to assist human decisions has increased in many professional fields such as justice 1 , 2 , personnel selection 3 , 4 and healthcare 5 – 8 . In the medical realm, the advent of AI-based decision support systems has been welcomed as a promise to minimize errors in human decision-making 9 – 13 .…”