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Handbook of Research on Artificial Intelligence in Human Resource Management
This cutting-edge Handbook offers a comprehensive introduction to the emerging research field of artificial intelligence (AI) in human resource management (HRM). Broadly mapping AI fields relevant for HR, it not only considers the more well-known areas of machine learning and natural language processing, but also lesser-known fields such as affective computing and robotic process automation.
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Critical Acclaim
Contributors
Contents
More Information
This cutting-edge Handbook offers a comprehensive introduction to the emerging research field of artificial intelligence (AI) in human resource management (HRM). Broadly mapping AI fields relevant for HR, it not only considers the more well-known areas of machine learning and natural language processing, but also lesser-known fields such as affective computing and robotic process automation.
Expert contributors analyze the applications of machine learning in human resources, including machine learning on text data, audio and video data, social media data, and in recruiting and staffing. They also explore a range of innovative topics such as knowledge representation and reasoning, and evolutionary computing. Discussing the explainability, fairness, accountability, and legitimacy of AI in HR, chapters bring normative issues to the fore. Approaches to researching AI in HR and to employing AI in HR research are also tackled. Offering an insight into existing research on artificial intelligence in human resources, the Handbook introduces core issues and considers implications for future research.
This Handbook will be critical reading for scholars and students of human resource management, knowledge management, organizational innovation, computer science, and information systems. It will also be beneficial for practitioners in these fields.
Expert contributors analyze the applications of machine learning in human resources, including machine learning on text data, audio and video data, social media data, and in recruiting and staffing. They also explore a range of innovative topics such as knowledge representation and reasoning, and evolutionary computing. Discussing the explainability, fairness, accountability, and legitimacy of AI in HR, chapters bring normative issues to the fore. Approaches to researching AI in HR and to employing AI in HR research are also tackled. Offering an insight into existing research on artificial intelligence in human resources, the Handbook introduces core issues and considers implications for future research.
This Handbook will be critical reading for scholars and students of human resource management, knowledge management, organizational innovation, computer science, and information systems. It will also be beneficial for practitioners in these fields.
Critical Acclaim
‘This Handbook is a must-have whether you know a little or a lot about AI and human resource management. Topics range from the highly technical for specialists to the more foundational for novices. Readers can dive in to get answers to specific questions or read the whole volume to gain a thorough grounding. AI is here to stay in human resource management. It poses many challenges for scholars and practitioners. This Handbook is a great guide for addressing those challenges.''
– Mark Lengnick-Hall, University of Texas at San Antonio, US
‘The potential for better decisions in managing people and also the conflicts between AI principles and those that have governed human resources are profound. This Handbook offers the most detailed and wide-ranging account available as to what AI solutions look like in this realm, not just those available now but most importantly those in the works.’
– Peter Cappelli, University of Pennsylvania, US
‘This Handbook provides a comprehensive overview of how AI might be deployed in the field of human resources and includes incisive analysis of some of the key challenges: explaining AIs’ decisions, fairness and legal regulation. It is a really excellent resource.’
– Andy Charlwood, University of Leeds, UK
– Mark Lengnick-Hall, University of Texas at San Antonio, US
‘The potential for better decisions in managing people and also the conflicts between AI principles and those that have governed human resources are profound. This Handbook offers the most detailed and wide-ranging account available as to what AI solutions look like in this realm, not just those available now but most importantly those in the works.’
– Peter Cappelli, University of Pennsylvania, US
‘This Handbook provides a comprehensive overview of how AI might be deployed in the field of human resources and includes incisive analysis of some of the key challenges: explaining AIs’ decisions, fairness and legal regulation. It is a really excellent resource.’
– Andy Charlwood, University of Leeds, UK
Contributors
Contributors: William J. Becker, Constant D. Beugré, Chulin Chen, Raphael de Barros Fritz, Alberto Fernández, Carmen Fernández-Martinez, Peter Fettke, Sandra L. Fisher, Felix Gross, Jake T. Harrison, Christopher J. Hartwell, Garret N. Howardson, Richard D. Johnson, Charlotte Köhler, Cornelius König, Richard Landers, Markus Langer, Sven Laumer, Christian Maier, Jorge Martinez-Gil, Florian Meier, Stefan Morana, Franziska Raudonat, Maarten Renkema, Dianna L. Stone, Stefan Strohmeier, Sarah E. Tuskey, Tim Weitzel, Kai von Lewinski, Lena Wolbeck, Katharina A. Zweig
Contents
Contents:
Preface xii
1 Artificial intelligence in human resources – an introduction 1
Stefan Strohmeier
PART I APPLICATIONS OF ARTIFICIAL INTELLIGENCE IN
HUMAN RESOURCES
PART I.1 APPLICATIONS OF MACHINE LEARNING IN HUMAN RESOURCES
2 HR machine learning – an introduction 25
Stefan Strohmeier
3 HR machine learning on text data 46
Felix Gross
4 HR machine learning on audio and video data 68
Carmen Fernández-Martinez and Alberto Fernández
5 HR machine learning on social media data 89
Jake T. Harrison and Christopher J. Hartwell
6 HR machine learning in recruiting 105
Sven Laumer, Christian Maier, and Tim Weitzel
7 Machine learning in HR staffing 127
Florian J. Meier and Sven Laumer
8 Machine learning in personnel selection 149
Cornelius J. König and Markus Langer
PART I.2 FURTHER APPLICATIONS OF ARTIFICIAL
INTELLIGENCE IN HUMAN RESOURCES
9 HR knowledge representation and reasoning 169
Jorge Martinez-Gil
10 HR robotic process automation 187
Peter Fettke and Stefan Strohmeier
11 HR evolutionary computing 207
Lena Wolbeck and Charlotte Köhler
12 HR natural language processing – conceptual overview and state of the
art on conversational agents in human resources management 226
Sven Laumer and Stefan Morana
13 HR affective computing 243
William J. Becker, Sarah E. Tuskey, and Constant D. Beugré
PART II CONSEQUENCES OF ARTIFICIAL INTELLIGENCE IN
HUMAN RESOURCES
14 Consequences of artificial intelligence in human resource management 261
Maarten Renkema
PART III NORMATIVE ISSUES OF ARTIFICIAL INTELLIGENCE IN
HUMAN RESOURCES
15 Explainability of artificial intelligence in human resources 285
Markus Langer and Cornelius J. König
16 Fairness of artificial intelligence in human resources – held to a higher
standard? 303
Sandra L. Fisher and Garret N. Howardson
17 Accountability of artificial intelligence in human resources 323
Katharina A. Zweig and Franziska Raudonat
18 Legitimacy of artificial intelligence in human resources – the legal
framework for using artificial intelligence in human resource management 337
Kai von Lewinski and Raphael de Barros Fritz
PART IV RESEARCH ISSUES OF ARTIFICIAL INTELLIGENCE IN
HUMAN RESOURCES
19 Design considerations for conducting artificial intelligence research in
human resource management 353
Richard D. Johnson and Dianna L. Stone
20 Employing artificial intelligence in human resources research 371
Chulin Chen and Richard Landers
Index 392
Preface xii
1 Artificial intelligence in human resources – an introduction 1
Stefan Strohmeier
PART I APPLICATIONS OF ARTIFICIAL INTELLIGENCE IN
HUMAN RESOURCES
PART I.1 APPLICATIONS OF MACHINE LEARNING IN HUMAN RESOURCES
2 HR machine learning – an introduction 25
Stefan Strohmeier
3 HR machine learning on text data 46
Felix Gross
4 HR machine learning on audio and video data 68
Carmen Fernández-Martinez and Alberto Fernández
5 HR machine learning on social media data 89
Jake T. Harrison and Christopher J. Hartwell
6 HR machine learning in recruiting 105
Sven Laumer, Christian Maier, and Tim Weitzel
7 Machine learning in HR staffing 127
Florian J. Meier and Sven Laumer
8 Machine learning in personnel selection 149
Cornelius J. König and Markus Langer
PART I.2 FURTHER APPLICATIONS OF ARTIFICIAL
INTELLIGENCE IN HUMAN RESOURCES
9 HR knowledge representation and reasoning 169
Jorge Martinez-Gil
10 HR robotic process automation 187
Peter Fettke and Stefan Strohmeier
11 HR evolutionary computing 207
Lena Wolbeck and Charlotte Köhler
12 HR natural language processing – conceptual overview and state of the
art on conversational agents in human resources management 226
Sven Laumer and Stefan Morana
13 HR affective computing 243
William J. Becker, Sarah E. Tuskey, and Constant D. Beugré
PART II CONSEQUENCES OF ARTIFICIAL INTELLIGENCE IN
HUMAN RESOURCES
14 Consequences of artificial intelligence in human resource management 261
Maarten Renkema
PART III NORMATIVE ISSUES OF ARTIFICIAL INTELLIGENCE IN
HUMAN RESOURCES
15 Explainability of artificial intelligence in human resources 285
Markus Langer and Cornelius J. König
16 Fairness of artificial intelligence in human resources – held to a higher
standard? 303
Sandra L. Fisher and Garret N. Howardson
17 Accountability of artificial intelligence in human resources 323
Katharina A. Zweig and Franziska Raudonat
18 Legitimacy of artificial intelligence in human resources – the legal
framework for using artificial intelligence in human resource management 337
Kai von Lewinski and Raphael de Barros Fritz
PART IV RESEARCH ISSUES OF ARTIFICIAL INTELLIGENCE IN
HUMAN RESOURCES
19 Design considerations for conducting artificial intelligence research in
human resource management 353
Richard D. Johnson and Dianna L. Stone
20 Employing artificial intelligence in human resources research 371
Chulin Chen and Richard Landers
Index 392
