Designing Recruitment Automation System to Reduce Gender, Age, Race, and Educational Background Bias in Indonesia
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Abstract
Research aim: This research aims to design a fair, technology-based recruitment system by eliminating bias and assessing candidates solely based on their competence.
Design/Method/Approach: This research utilizes the ADDIE model to develop a bias-free recruitment system.
Research Finding: The system effectively identifies and minimizes bias in candidate selection by prioritizing relevant qualifications and competencies, while excluding non-work-related factors.
Theoretical contribution/Originality: This research enhances the understanding of automation in employee recruitment in developing countries, particularly Indonesia, by introducing innovative recruitment algorithms that promote diversity and inclusivity across various companies.
Practical/Policy Implication: This research suggests that companies adopt a more equitable automation system to foster a diverse and innovative work environment.
Research limitation: This study requires further validation with large-scale, real-world data. Future research should utilize more diverse datasets and explore the system’s impact in a broader context.
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References
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