Feb 15, 2024
The authors reviewed relevant bodies of literature on machine learning (ML) and human resource management (HRM), examined Department of the Air Force (DAF) personnel policy documents, and tested ML systems that deliver recommendations to officer developmental education and promotion boards. They were then able to demonstrate the feasibility of applying ML to existing DAF HRM processes.
Volume 1, Key Findings and Recommendations for Policymakers
Published Feb 12, 2024
The national security environment poses strategic challenges for Department of the Air Force (DAF) human resource management (HRM) policies and systems. While the DAF has historically leveraged data analysis to make decisions, such technologies as machine learning (ML) and natural language processing (NLP) could offer opportunities to improve decisionmaking in HRM.
Evidence from other organizations shows that, while adoption of these technologies in general is high, adoption in the area of HRM remains uncommon because of challenges unique to the HRM domain.
Leveraging data technologies for the kind of strategic impact that senior leaders are calling for requires DAF decisionmakers to systematically select the right mix of projects, effectively execute the development of selected projects, establish procedures for testing decision-support systems to address ethical and legal unknowns, and successfully transition systems into use in a way that is acceptable to the adopting organizations. To address these elements, the authors used a mixed-methods approach that involved (1) reviewing relevant bodies of literature on ML and HRM; (2) examining DAF personnel policy documents; and (3) implementing and testing ML systems that deliver recommendations to officer developmental education and promotion boards. From this review, they extracted evidence-based practices for innovation management and technology transition, along with principles for safe and ethical use of data technologies. From the analysis of DAF personnel policies, they identified high-value use cases. Finally, from their technical case studies, they demonstrated the feasibility of applying ML to existing DAF HRM processes.