The Personnel Records Scoring System: Volume 3, A Methodology for Designing Tools to Support Air Force Human Resources Decisionmaking

Format:

Book

Author/Creator:

Schulker, David

Contributor:

Montemayor, Cheryl K.

Walsh, Matthew

Williams, Joshua

Zhang, Li Ang

Language:

English

Other Title:

Personnel Records Scoring System

Place of Publication:

RAND Corporation 2024

Summary:

The Department of the Air Force (DAF) maintains rich records on the knowledge, skills, abilities, and other attributes of its personnel. The personnel system records much of this information as structured lists and free-form text, which creates a need for many processes in which human resources decisionmakers must review the records by hand before issuing a decision. These processes become more difficult with larger populations of officers, as any human judge faces capacity limitations. Thus, there is an opportunity for artificial intelligence applications to improve the quality of inputs for these review processes, helping the human resources management system to become more effective and/or more efficient in meeting DAF strategic goals. To develop a computational approach to standardize and extract meaning from textual records, the authors reviewed state-of-the-art natural language processing (NLP) and machine learning (ML) approaches. They applied these approaches to text from officer performance reports (OPRs) and used them to predict O-5 and O-6 promotion outcomes and Developmental Education Designation Board scores. The resulting system created for this research is called the Personnel Records Scoring System (PReSS). Aside from the use of ML models to predict board results for future candidates, the authors propose a methodology for using models to generate summary reports that highlight the most-significant statements and detractors contained in a service member's records.