Cover: Leveraging Machine Learning to Improve Human Resource Management

Leveraging Machine Learning to Improve Human Resource Management

Volume 1, Key Findings and Recommendations for Policymakers

Published Feb 12, 2024

by David Schulker, Matthew Walsh, Avery Calkins, Monique Graham, Cheryl K. Montemayor, Albert A. Robbert, Sean Robson, Claude Messan Setodji, Joshua Snoke, Joshua Williams, et al.

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Research Questions

  1. How can the DAF build and oversee a portfolio of research and development projects exploring the use of ML in HRM?
  2. How can the DAF effectively develop decision-support systems based on NLP?
  3. How can the DAF test decision-support systems to confirm they are safe to use in decisionmaking?
  4. How can the DAF strategically transition systems for operational use once they are developed and tested?

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.

Key Findings

  • To generate business value by meaningfully contributing to HRM process efficiency and workforce capabilities, the DAF must first grow an ML project portfolio made up of technically feasible projects that address near-term and future HRM needs.
  • To effectively develop ML systems, the DAF must first specify HRM objectives and then select modes of decision support that meet those objectives.
  • To act legally, ethically, and responsibly, the DAF must test candidate systems to ensure that they are safe—that is, accurate, fair, and explainable.
  • To overcome inertia, the DAF must pursue transition pathways that involve gradually increasing the degree of ML influence or, alternatively, gradually increasing the significance of the HRM processes at stake.


  • Manage innovation constantly: Require a well-formulated business case and technical feasibility assessment before projects can move forward. Adopt a portfolio approach to managing complexity.
  • Develop effectively: Begin the design process with priority objectives and consider multiple modes of decision assistance. Prioritize development of ML systems that automatically summarize narrative records as a mode of decision support.
  • Implement safely: Use an accurate-fair-explainable framework to create tailored designs that safely meet objectives. Publish acceptable limits for safety criteria in different classes of use cases to encourage adoption.
  • Transition strategically: Regulate the stakes of the HRM decision and the amount of influence allotted to the ML system to find an implementation that balances value and risk. Apply ML systems to limited cases before gradually expanding their scope and consequence.

Research conducted by

This research was prepared for the Department of the Air Force and conducted within the Workforce, Development, and Health Program of RAND Project AIR FORCE.

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