Algorithmic Bias in AI-Driven Applicant Tracking Systems and Human Resource Diversity Hiring in Oil and Gas Firms: Evidence from Rivers State, Nigeria

The rapid diffusion of artificial intelligence (AI) in human resource management has transformed how organizations recruit and screen candidates. Yet growing evidence suggests that these systems can perpetuate, and sometimes amplify, pre-existing biases — quietly disadvantaging women, ethnic minorities, and other underrepresented groups at the very first stage of the hiring funnel. This study examined the impact of algorithmic bias in AI-driven applicant tracking systems (ATS) on human resource diversity hiring in oil and gas firms in Rivers State, Nigeria. Three dimensions of algorithmic bias were investigated: gender-biased screening algorithms, ethnicity-linked ranking distortions, and training-data bias. Drawing on Algorithmic Accountability Theory and the Institutional Theory of Organizations, the study adopted a cross-sectional survey design. A structured questionnaire was administered to 243 HR practitioners, recruitment officers, and diversity managers purposively and randomly drawn from three major oil and gas firms in Rivers State. Simple linear regression was used to test the three hypotheses. Results showed that gender biased screening algorithms, ethnicity-linked ranking distortions, and training-data bias each had a significant negative effect on diversity hiring outcomes. The study concluded that unchecked algorithmic bias in AI-driven ATS constitutes a structural barrier to workforce diversity in the oil and gas sector and that deliberate bias-mitigation strategies are urgently needed. Practical recommendations are offered for HR professionals, technology vendors, and industry regulators.