| Summary: | Abstract This study aims to address the research gap on algorithmic discrimination caused by AI-enabled recruitment and explore technical and managerial solutions. The primary research approach used is a literature review. The findings suggest that AI-enabled recruitment has the potential to enhance recruitment quality, increase efficiency, and reduce transactional work. However, algorithmic bias results in discriminatory hiring practices based on gender, race, color, and personality traits. The study indicates that algorithmic bias stems from limited raw data sets and biased algorithm designers. To mitigate this issue, it is recommended to implement technical measures, such as unbiased dataset frameworks and improved algorithmic transparency, as well as management measures like internal corporate ethical governance and external oversight. Employing Grounded Theory, the study conducted survey analysis to collect firsthand data on respondents’ experiences and perceptions of AI-driven recruitment applications and discrimination.
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