Candidate Perspectives on AI-Driven Hiring: Navigating Ethical Concerns

Abstract/Description

Emerging technologies are reshaping the industries across all sectors, including human resource management (HRM). It has transformed the recruitment process by using artificial intelligence based technologies which is enhancing efficiency, reducing cost and improving candidate matching process. While AI based recruitment has improved the efficiency, it has also raised significant ethical concerns from the candidates’ perspective. The training of AI systems with historical hiring data could strengthen existing bias which might create discriminatory situations during candidate selection phases. The decisions taken by the AI algorithms have often been found to increase the biases that leads towards the discriminatory results. The research aims to reveal applicant views regarding AI recruitment systems through an evaluation of their AI-based hiring preferences and a review of ethical elements that affect advanced recruitment technology acceptance. Different theories have been discussed in the literature review including the technology acceptance model (TAM), unified theory of acceptance & use of technology (UTAUT), organizational justice theory, deontological ethics, stakeholder theory and value sensitive design. These theories generate various variables which are significant for the study. The extensive research investigates recruitment systems which require clarity as well as moral integrity and a candidate-centered approach. A structured close ended questionnaire (Likert scale) was developed and a survey method was used to collect the data from both prospective and past candidates who have experienced AI driven hiring tools. The non-probabilistic method was employed to draw a sample and data was analyzed using the multiple linear regression (MLR) statistical technique via SPSS.

Keywords

Artificial Intelligence Tools, AI-Based Recruitment, Candidates’ Perspective, and Ethical Concerns

Track

Management

Session Number/Theme

Management - Session I

Session Chair

Dr. Khalid Basit

Start Date/Time

14-6-2025 10:55 AM

End Date/Time

14-6-2025 12:35 PM

Location

MCC 14 Ground Floor, AMAN CED Building

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Jun 14th, 10:55 AM Jun 14th, 12:35 PM

Candidate Perspectives on AI-Driven Hiring: Navigating Ethical Concerns

MCC 14 Ground Floor, AMAN CED Building

Emerging technologies are reshaping the industries across all sectors, including human resource management (HRM). It has transformed the recruitment process by using artificial intelligence based technologies which is enhancing efficiency, reducing cost and improving candidate matching process. While AI based recruitment has improved the efficiency, it has also raised significant ethical concerns from the candidates’ perspective. The training of AI systems with historical hiring data could strengthen existing bias which might create discriminatory situations during candidate selection phases. The decisions taken by the AI algorithms have often been found to increase the biases that leads towards the discriminatory results. The research aims to reveal applicant views regarding AI recruitment systems through an evaluation of their AI-based hiring preferences and a review of ethical elements that affect advanced recruitment technology acceptance. Different theories have been discussed in the literature review including the technology acceptance model (TAM), unified theory of acceptance & use of technology (UTAUT), organizational justice theory, deontological ethics, stakeholder theory and value sensitive design. These theories generate various variables which are significant for the study. The extensive research investigates recruitment systems which require clarity as well as moral integrity and a candidate-centered approach. A structured close ended questionnaire (Likert scale) was developed and a survey method was used to collect the data from both prospective and past candidates who have experienced AI driven hiring tools. The non-probabilistic method was employed to draw a sample and data was analyzed using the multiple linear regression (MLR) statistical technique via SPSS.