Algorithmic Hiring and Workplace Diversity: Evidence from a Zambian Private Sector Multinational

Algorithmic hiring systems are increasingly adopted to enhance recruitment efficiency; however, their implications for workplace diversity and equitable representation remain underexplored in Sub-Saharan African contexts. This study examined the relationship between algorithmic hiring and workplace diversity at Carlcare Service Limited in Zambia, focusing on adoption patterns, mechanisms of algorithmic bias, and governance practices. A convergent mixed-methods design was employed, combining quantitative data from 121 employees collected through structured questionnaires with qualitative insights from eight key informants, including HR managers, recruitment specialists, IT administrators, and senior management. Findings revealed that algorithmic hiring adoption was primarily driven by efficiency considerations rather than diversity objectives, with CV screening identified as the dominant application stage. Perceptions of diversity improvement were largely neutral, with ethnic diversity recording the weakest outcomes, and 39.7% of respondents indicating that algorithmic tools may filter out qualified candidates with atypical profiles. Qualitative results highlighted a lack of diversity-oriented governance frameworks, limited bias mitigation mechanisms, and cultural misalignment of imported algorithmic systems with local labour market dynamics. The study concludes that algorithmic hiring systems developed in Western institutional environments require contextual adaptation through socio-technical governance frameworks to ensure fairness and inclusivity in African settings. It recommends the integration of diversity mandates, bias auditing mechanisms, and localized system design to enhance equitable recruitment outcomes.