The traditional recruitment process can be disrupted and make it impossible to increase the number of diverse workers through unconscious bias, as a result of that, the long-term performance of the organization will decrease. This paper investigates how AI can be used to reduce unconscious bias and create teams that are both high-performing and diverse.This research paper analyzes the effect of using Sociotechnical Systems Theory and Resource-Based View (RBV) approaches to standardizing recruitment processes at their earliest stage via AI technology to remove cognitive bias as measured by Implicit Association Tests (IAT) using Unilever’s 2016 updates, showing how algorithmic or AI-based standardization can increase the diversity of new hires while decreasing recruitment costs significantly.The main finding of this research is the existence of one of the largest risks posed by algorithmic bias, particularly Integrity of Data: GIGO or Garbage In and Garbage Out relating to data quality. Overall, the research identified that while AI provides a viable option for improving fairness and facilitating improved efficiency, it should always be considered within a Human-In-The-Loop (HITL) Framework. Therefore, the ultimate liability for ethical review and final decision will remain with a HR (Human Resources) Manager
Ivan Dario Gutierrez Zambrano (Wed,) studied this question.