Our paper contributes to the literature in three ways. First, we comprehensively organize the extant research on ethical considerations of AI recruiting by identifying and summarizing the different perspectives taken. Second, we make accessible to researchers and human resources (HR) professionals an overview of the ethical considerations in AI recruiting by synthesizing extant research. We thereby ethically evaluate these considerations and classify them into ethical opportunities, risks, and ambiguities, developing an ethical framework of AI recruiting. Third, we identify current research gaps and propose moral topics and questions that call for a deeper exploration in both theoretical and empirical future research.
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Extant literature refers to various organizational standards that firms may and should implement to ensure ethical use of AI in recruiting. First, it is suggested that companies applying AI tools in the personnel selection process comply with privacy laws just as they would in traditional hiring. On the one hand, this means that organizations should fully protect and keep safe all sensitive data. On the other hand, recruiters should not use or predict any private or sensitive candidate information in the recruiting process. In addition, firms should proactively and fully brief candidates that their data will be analyzed by AI systems and obtain their consent (e.g., Chamorro-Premuzic & Akhtar, 2019; Simbeck, 2019). Second, firms should proactively and explicitly provide meaningful information on the hiring decision-making process, including information about the algorithmic techniques and data sets used, to ensure transparency and craft effective policy (Köchling et al., 2020; Raghavan et al., 2020; Sánchez-Monedero et al., 2020). Additionally, it should be always transparent to applicants whether they are communicating with another human or with AI (Simbeck, 2019). Third, several papers (e.g., Chamorro-Premuzic & Akhtar, 2019; Köchling et al., 2020) also suggested human oversight on AI as a standard for organizations. The authors encouraged a human review, in which experienced recruiters oversee the selection and evaluation made by AI. They argued that decisions should be made by an algorithm-informed human, rather than by an algorithm alone. Fourth, to further ensure and audit the implementation of these ethical standards, various authors have referred to compliance instruments companies should establish, such as an AI ethics board with an oversight function, consisting of representatives of relevant stakeholders who debate the data and ethical dimensions of AI algorithms and agree on boundaries for AI technology in the company (Simbeck, 2019; Tambe et al., 2019). In addition, Tambe et al. (2019) recommended specifying a code of ethics for AI-related initiatives within the company. Lastly, authors have encouraged diverse data scientist teams in organizations to foster inclusion and equity in AI (Giang, 2018; Yarger et al., 2020). In particular, in the ML algorithm development process, diverse voices across gender and race must be present to raise questions and check implicit assumptions. 2ff7e9595c
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