RAND Corporation
Safe Use of Machine Learning for Air Force Human Resource Management : Evaluation Framework and Use Cases, Volume 4
Product Code:
9781977412898
ISBN13:
9781977412898
Condition:
New
$39.08
Private-sector companies are applying artificial intelligence (AI) and machine learning (ML) to diverse business functions, including human resource management (HRM), to great effect. The Department of the Air Force (DAF) is poised to adopt new analytic methods, including ML, to transform key aspects of HRM. Yet ML systems, as compared with other information technologies, present distinct safety concerns when applied to HRM because they do not use well-understood, preprogrammed rules set by human resources experts to achieve objectives. The DAF cannot confidently move forward with valuable AI and ML systems in the HRM domain without an analytic framework to evaluate and augment the safety of these systems. To understand the attributes needed to apply ML to HRM in a responsible and ethical manner, the authors reviewed relevant bodies of literature, policy, and DAF documents. From the they developed an analytic framework centered on measuring and augmenting three attributes of ML systems: accuracy, fairness, and explainability. In this report, the authors define safety by these three qualities. They then applied a case study approach; they developed ML systems and exercised the framework using the examples of officer promotion and developmental education boards.
| Author: Joshua Snoke, Matthew Walsh, Joshua Williams |
| Publisher: RAND Corporation |
| Publication Date: Apr 30, 2024 |
| Number of Pages: NA pages |
| Language: English |
| Binding: Paperback |
| ISBN-10: 1977412890 |
| ISBN-13: 9781977412898 |
Safe Use of Machine Learning for Air Force Human Resource Management : Evaluation Framework and Use Cases, Volume 4
$39.08
Private-sector companies are applying artificial intelligence (AI) and machine learning (ML) to diverse business functions, including human resource management (HRM), to great effect. The Department of the Air Force (DAF) is poised to adopt new analytic methods, including ML, to transform key aspects of HRM. Yet ML systems, as compared with other information technologies, present distinct safety concerns when applied to HRM because they do not use well-understood, preprogrammed rules set by human resources experts to achieve objectives. The DAF cannot confidently move forward with valuable AI and ML systems in the HRM domain without an analytic framework to evaluate and augment the safety of these systems. To understand the attributes needed to apply ML to HRM in a responsible and ethical manner, the authors reviewed relevant bodies of literature, policy, and DAF documents. From the they developed an analytic framework centered on measuring and augmenting three attributes of ML systems: accuracy, fairness, and explainability. In this report, the authors define safety by these three qualities. They then applied a case study approach; they developed ML systems and exercised the framework using the examples of officer promotion and developmental education boards.
| Author: Joshua Snoke, Matthew Walsh, Joshua Williams |
| Publisher: RAND Corporation |
| Publication Date: Apr 30, 2024 |
| Number of Pages: NA pages |
| Language: English |
| Binding: Paperback |
| ISBN-10: 1977412890 |
| ISBN-13: 9781977412898 |
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