Artificial Intelligence and Public Human Resource Management: Questions for Research and Practice

• Author: Brad A. M. Johnson,Jerrell D. Coggburn,Jared J. Llorens
• DOI: http://doi.org/10.1177/00910260221126498
• Published date: 01 December 2022
• Date: 01 December 2022
• Subject Matter: Articles

https://doi.org/10.1177/00910260221126498

Public Personnel Management

2022, Vol. 51(4) 538 –562

© The Author(s) 2022

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DOI: 10.1177/00910260221126498

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Article

Artificial Intelligence and

Public Human Resource

Management: Questions for

Research and Practice

Brad A. M. Johnson1, Jerrell D. Coggburn2,

and Jared J. Llorens3

Abstract

Advances in big data and artificial intelligence (AI), including machine learning (ML)

and other cognitive computing technologies (CCT), have facilitated the development

of human resource management (HRM) applications promising greater efficiency,

economy, and effectiveness for public administration (Maciejewski, 2017) and better

alignment with the modern, constantly evolving employment landscape. It is not

surprising then that these advanced technologies are featured in proposals to elevate

the government’s human capital. This article discusses current and emerging AI

applications that stand to impact most (if not all) HRM functions and their prospects

for elevating public human capital. In particular, this article (a) reviews the current

state of the field with regards to AI and HRM, (b) discusses AI’s current and potential

impact upon the core functional areas of HRM, (c) identifies the main challenges

AI poses to such concerns as public values, equity, and traditional merit system

principles, and (d) concludes by identifying research needs for public HRM scholarship

and practice that highlight the growing role and influence of AI applications in the

workplace.

Keywords

artificial intelligence (AI), public human resource management, public human capital

1University of Nevada, Reno, USA

2North Carolina State University, Raleigh, USA

3Louisiana State University, Baton Rouge, USA

Corresponding Author:

Jerrell D. Coggburn, Department of Public Administration, School of Public & International Affairs, North

Carolina State University, Campus Box 8102, Raleigh, NC 27695-8102, USA.

Email: jcoggburn@ncsu.edu

1126498PPMXXX10.1177/00910260221126498Johnson et al.Public Personnel Management

research-article2022

Johnson et al. 539

Introduction

To achieve important public purposes and create public value, public organizations

must recruit, hire, motivate, develop, and retain a talented workforce. Yet, despite the

imperative of strategic human capital management, dissatisfaction with traditional

merit-based civil service systems and human resource management (HRM) practices

has been long-standing and pervasive among public employers at all levels of govern-

ment in the United States. Calls to address such dissatisfaction have taken on urgency

in recent years, as evidenced by the National Academy of Public Administration’s

(NAPA) recent two-part report, No Time to Wait: Building a Public Service for the

21st Century (2017, 2018). Likewise, the International Public Management Association

for Human Resources’ (IPMA-HR) 20/20 Task Force (2017) identifies several strate-

gies to facilitate public HRM’s move from transactional to transformative. NAPA’s

(2021) Elevating Human Capital outlined an aggressive plan for the U.S. Office of

Personnel Management (OPM) to lead the federal government’s human capital trans-

formation. These reports propose nothing short of wholesale change—from a culture

of compliance and control to one of performance. Specifically, OPM’s 2022 Federal

Workforce Priorities Report prioritizes leveraging technology, modernizing IT pro-

cesses, and strategically leveraging data (U.S. Office of Personnel Management

[OPM], 2022). This emphasis leaves little question that artificial intelligence (AI) will

play prominently in efforts to transform federal human capital management (Gerton &

Mitchell, 2019; Llorens, 2021).

While calls to reform public HRM are not new, contemporary discussions are dis-

tinguished by the central role of advanced technology in the proposed remedies.

Building on earlier operational and relational phases of Human Resource Information

System (HRIS) development that saw the integration and expansion of multiple data

elements of HRM in large databases (Valcik et al., 2021), interest has turned to meth-

ods for effectively taking advantage of copious and available data. Advances in big

data and AI, including machine learning (ML) and other cognitive computing tech-

nologies (CCT), have facilitated the development of applications promising greater

efficiency, economy, and effectiveness for public administration (Maciejewski, 2017)

and better alignment with the modern, constantly evolving employment landscape.

For many HRM functions, where processes and decision-making are often accompa-

nied by ambiguity and risk, the promise and possibilities associated with AI may sound

almost too good to be true. As such, much like the nascent state of AI research in pub-

lic administration (Bullock, 2019; Giest & Klievink, 2022; Maciejewski, 2017;

Pencheva et al., 2020; Young et al., 2019), important questions remain for researchers

and practitioners about the benefits and challenges of AI in the public HRM context.

The set of tools and procedures associated with AI raises concerns of bias and dis-

crimination similar to other technological tools. As such, algorithmic bias has been sub-

ject to increased research scrutiny (Agarwal, 2018). The allure of AI tools is their

purported ability to provide seemingly certain predictions based on copious data and

learning techniques that may evolve independent of administrative oversight. Data col-

lection, including the choice of which metrics to collect and who to collect it from,