Recruitment and selection are key functions of human resources management (HRM); the
recruitment process in HRM involves the selection of suitable candidates, application
management, and interview management. As technology advances continuously, businesses
must continually grow and evolve. HR strategies must also keep pace with this transformation
in many business areas. A key aspect of this evolution is recruitment automation,
utilising technology to enhance and modernise the hiring process. Artificial Intelligence
and Machine Learning shape the future of businesses and their functions, including
human resources functions, which are increasingly integrated into the recruitment
process. Technologies can swiftly scrutinise enormous volumes of applications, resumes,
and CVs, speed up the hiring process, enhance the candidate experience through pre-assessment
automation tools, and support the decision-making process through data analytics.
However, the human touch brings empathy, understanding, and critical thinking to the
table. The benefits of automated recruitment are well-documented, demonstrating their
ability to reduce administrative burdens and expedite the hiring process effectively.
However, automated recruitment systems have flaws, including concerns about trustworthiness,
security, and bias. These issues can arise during the design phase, deployment phase,
or interpretation of results. The bias issue is particularly crucial, yet the literature
has a gap in its exploration. This paper aims to fill this gap by examining various
cognitive biases and presenting the human element effect within automated recruitment
processes. The paper reviews and analyses existing literature of academic articles
relevant to the subject matter and connects technological dimensions of HRM within
automated recruitment processes with literature related to behavioural science. It
contributes to bridging the gap in the literature on biases caused by automated recruitment
functions by shedding and analysing the human element effect, precisely, cognitive
biases that may arise if human intervention in the integration process technology
in recruitment processes. This research is novel and unique, offering fresh insights
and perspectives that have not been explored before, making it a compelling read for
those interested in the intersection of technology, HRM, and behavioural science.
The study's findings suggest that the automated recruitment process has various cognitive
bias sources, from technology designers, domain experts, and end users or receivers.
As a result, the paper suggests recommendations for organisations and future researchers
to explore strategies to mitigate biases associated with these systems.
