Designing Recruitment Automation System to Reduce Gender, Age, Race, and Educational Background Bias in Indonesia - Nisya, Nur Aeinun; Riani, Lilia Pasca (Universitas Nusantara PGRI Kediri, 2025)
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.