DAVID SCHULKER, MATTHEW WALSH, AVERY CALKINS, MONIQUE GRAHAM, CHERYL K. MONTEMAYOR,
ALBERT A. ROBBERT, SEAN ROBSON, CLAUDE MESSAN SETODJI, JOSHUA SNOKE, JOSHUA WILLIAMS,
LI ANG ZHANG
Leveraging Machine
Learning to Improve
Human Resource
Management
Volume 1, Key Findings and
Recommendations for Policymakers
T
he national security environ-
ment poses strategic chal-
lenges for human resource
management (HRM) policies
and systems. Then-Air Force Chief
of Staff Gen Charles Brown (Chair-
man of the Joint Chiefs of Staff as of
this writing) captured this challenge
succinctly in the first of his “action
orders” in September 2020:
Past success is no guarantee
of future performance. The
[U.S. Air Force] must ensure
the future force reflects the
identity and attributes required
for success in the high-end
fight. Tomorrow’s Airmen
must be organized, trained,
and equipped to succeed in the
KEY FINDINGS
■ To generate business value by meaningfully contributing to human
resource management (HRM) process efficiency and workforce
capabilities, the U.S. Deparrtment of the Air Force (DAF) must first
grow a machine learning (ML) project portfolio made up of tech-
nically 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 candi-
date 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.
Research Report
