Global Journal of Management and Business Research, A: Administration and Management, Volume 23 Issue 2

III. G aps in R esearch Current literature appraises the impact of AI in HRM and related issues of job replacement to some extent, however, studies typically take a broad approach to these topics and do not engage with specifics. For example, no studies have so far surveyed the difficulty of using AI software tools and the impact of this on the sourcing, screening, and selection stages of the recruitment process. The literature also does not yet detail what specific aspects of each recruitment phase (e.g., sourcing, interviewing, decision-making) are covered by AI-based software. It further does not yet offer projected job loss statistics connected to using AI- based software for different job functions, nor does it cover job replacement risk, given that AI performs a job function which can directly cause professionals to retrain. * The abductive approach is an analytic induction approach for generating innovative ideas using inductive and deductive reasoning. Research Gaps in HR Applications of AI in Recruitment Some studies have investigated issues of bias in recruitment due to AI-based software, both in regard to pre-existing bias in the data that trains the AI, and bias added by a human during data labeling. Others examine ethical issues related to bias, such as sex, race, ethnicity, or religion, being included in recruitment decision making via the data given to the AI. However, the literature does not dig deep into various sources of bias to ascertain the root cause, and ignores related issues such as limited AI training data and short-term decision-making history. The literature also does not suggest any options for improving the AI-based software and reducing or removing the bias. It further does not cover the ethical impact on decisions made through AI inputs that typically ignore human-specific aspects. In summarizing these omissions, three concerning issues surface that require further In this regard, while literature offers some case studies of AI-based software being used in interviews, it does not cover job-function-specific AI-based interviews and does not investigate the efficacy of AI-based software in comparison with human interviews. The literature also does not survey the impacts of AI applications on both HR and non-HR teams in an organization. This is a vital element of understanding these impacts, since job functions are significantly different, as are their relat ed implications. IV. R esearch Q uestion and R esearch P roblem The literature reviewed for this paper suggests that AI-based tools and AIER bring efficiency and innovation to HRM, thus improving recruitment processes. AIER can be a game-changer, for (a) HR professionals, as it allows them to invest time in more valuable business goals; (b) organizations, as it scales recruiting up rapidly with lower bias; and (c) even job candidates, as it leads to predictable expectations between the applicant and the recruiter. Despite its benefits, however, certain key limitations can create risk for organizations in utilizing AIER. In addition, as a bleeding-edge technology, the use of AIER requires both specialized training and the adaptation of organizational processes, which means time and staff resources must be routed to this end. These limitations pose a costly investment for organizations, and evidence suggests that this has slowed AIER adoption in HRM. Given this complex situation, this paper poses the research question, Is there value in using artificial intelligence in the recruitment and selection process? V. R esearch M ethodology The literature review has described the essential theoretical elements and secondary data regarding the use of AI in HRM. Literature was obtained from various sources, including: google search; google scholar; researchgate.net; iopscience.iop.org ; various journal titles in business and management, system sciences, and tourism; emerald.com ; scirp.org ; the Journal of Critical Reviews ; and sciencedirect.com . Study of the literature provided insights into the various relevant research areas and assisted in identifying the research gaps, some of which may be addressed by the research proposed by this paper. The next phase of the research methodology will use surveys to gather data from HR teams in various companies with the intention of discovering the different uses of AI tools in HR recruitment functions. Survey data will be the primary data source, and additional secondary data will be garnered from existing research. To begin with, a sample size of HR employees will be established across various companies. Next, to conduct the survey, a questionnaire will sent to the identified HR employee sample (see Appendix for the proposed 56 Global Journal of Management and Business Research Volume XXIII Issue II Version I Year 2023 ( ) A © 2023 Global Journals as COVID-19 restrictions, faster paperwork, availability of technology, and reliable internet reachability. investigation: (a) AI-based software's lack of transparency in Ail earning and decision-making can magnify biases, impacting the talent pool; (b) AI can break the non-discrimination policy (based on sex, race, ethnicity, or religion) as it pulls this data from sourcing channels (such as LinkedIn, monster.com, etc.); and (c) compared to face-to-face human interviews, AI hiring decisions inherently ignore candidates' other abilities that may not be reflected in sourced data (such as resumes), potentially leading to inappropriate hiring decisions and/or under-performing talent.