Artificial Intelligence in Recruiting. A Literature Review on Artificial Intelligence Technologies, Ethical Implications and the Resulting Chances and Risks

Thèse de Master , 2020 , 130 Pages , Note: 1,3

Informatique - Intelligence artificielle

The thesis aims to analyze this field of tension between the benefits and risks caused by the introduction of AI into recruiting based on a systematic analysis of academic publications. To the knowledge of the author, neither a literature review on ethical challenges of AI-based recruiting tools nor an overview addressing application fields and the resulting ethical risks has been published so far. Consequently, this thesis aims to close this research gap by disclosing how AI technologies affect the recruiting process and how ethical challenges arising from the implementation of AI-based tools are addressed in the same publications.

Consequently, research question (RQ) one (RQ1) and RQ two (RQ2) can be derived: Which AI technologies are applied in the field of recruiting, and how do they influence the recruiting process? Which major ethical challenges arise from the introduction of AI into recruiting, and how are these challenges addressed by the proposed AI-based tools?

Addressing these two RQs, the remainder of this thesis is structured as follows. Chapter 2 classifies recruiting and its subprocesses as a part of Human Resources (HR), establishes a common understanding of AI and machine learning (ML) algorithms relevant in AI-based recruiting tools, and derives major ethical challenges with a focus on the ethical principles fairness and transparency. In chapter 3, the methodical approach used for identifying and selecting relevant literature is described. Chapter 4 answers both RQs, based on the AI-based recruiting tools included in the literature set. In turn, the first part of the analysis focuses on analyzing how the needs raised through traditional recruiting means are addressed by AI-based recruiting tools, also touching base on the underlying technologies. The second part addresses if and, where applicable, how these publications incorporate fairness and transparency. Subsequently, chapter 5 discusses the main findings of the analysis of the literature set and provides implications for theory and practice, followed by a brief conclusion and the outlining of limitations and future research fields in chapter 6.

Inhaltsverzeichnis (Table of Contents)

• 1 Introduction
• 1.1 Problem statement
• 1.2 Objective and structure of the thesis
• 2 Theoretical Background
• 2.1 Recruiting as a part of Human Resources
• 2.1.1 Recruiting and its subprocesses
• 2.1.1.1 Reaching out subprocess
• 2.1.1.2 Preselecting subprocess
• 2.1.1.3 Assessing subprocess
• 2.1.2 Evolution of digitalization in recruiting
• 2.2 Machine learning as a part of artificial intelligence (AI)
• 2.2.1 Central framework used to create machine learning models
• 2.2.2 Supervised learning algorithms used in AI-based recruiting tools
• 2.2.2.1 Machine learning using statistical models
• 2.2.2.2 Machine learning using instance-based models
• 2.2.2.3 Machine learning using decision tree models
• 2.2.2.4 Machine learning using neural network models
• 2.3 Relevant ethical principles affected by AI-based recruiting tools
• 2.3.1 Ethical principle fairness
• 2.3.2 Ethical principle transparency
• 3 Method
• 3.1 Phase 1 - Definition of review scope
• 3.2 Phase 2 - Conceptualization of the topic
• 3.3 Phase 3 - Literature search
• 3.3.1 Phase 3.1 - Identification of relevant journals and conferences
• 3.3.2 Phase 3.2 - Identification of search databases
• 3.3.3 Phase 3.3 - Keyword search
• 3.3.4 Phase 3.4 - Forward and backward search
• 4 Results
• 4.1 Analysis of AI-based recruiting tools
• 4.1.1 Enhancing the reaching out subprocess
• 4.1.1.1 Challenges in the reaching out subprocess
• 4.1.1.2 Need and approaches for identifying high potential passive candidates
• 4.1.1.3 Analysis of AI-based recruiting tools in the reaching out subprocess
• 4.1.2 Enhancing the preselecting subprocess
• 4.1.2.1 Challenges in the preselecting subprocess
• 4.1.2.2 Need and approaches for preselecting candidates
• 4.1.2.3 Need and approach for creating personalized questions for questionnaires
• 4.1.2.4 Analysis of Al-based recruiting tools in the preselecting subprocess
• 4.1.3 Enhancing the assessing subprocess
• 4.1.3.1 Challenges in the assessing subprocess
• 4.1.3.2 Need and approaches for objectivizing audio-visual input
• 4.1.3.3 Need and approaches for objectivizing the derivation of personal characteristics
• 4.1.3.4 Need and approaches for predicting job performance and working habits
• 4.1.3.5 Need and approaches for predicting the salary of candidates
• 4.1.3.6 Analysis of AI-based recruiting tools in the assessing subprocess
• 4.1.4 Concluding assessment of AI-based recruiting tools
• 4.2 Analysis of the addressing of ethical challenges arising from AI-based recruiting tools
• 4.2.1 Addressing of fairness
• 4.2.1.1 Challenge of using data of poor quality in AI-based recruiting tools
• 4.2.1.2 Need for addressing fairness in AI-based recruiting
• 4.2.1.3 Analysis of the addressing of fairness in AI-based recruiting tools
• 4.2.2 Addressing of transparency
• 4.2.2.1 Challenge of using black box models in AI-based recruiting tools
• 4.2.2.2 Need for addressing transparency in AI-based recruiting
• 4.2.2.3 Analysis of the addressing of transparency in in AI-based recruiting tools
• 4.2.3 Concluding assessment of the addressing of ethical challenges
• 5 Discussion
• 5.1 Implications for research
• 5.2 Implications for practice

Zielsetzung und Themenschwerpunkte (Objectives and Key Themes)

This master's thesis aims to conduct a comprehensive literature review on the utilization of artificial intelligence (AI) technologies in the field of recruiting. The study investigates the ethical implications of AI-based recruiting tools, analyzing the opportunities and risks associated with their implementation.

• The evolution of digitalization within the recruiting process
• The application of machine learning algorithms in AI-based recruiting tools
• The ethical implications of fairness and transparency in AI-based recruitment
• The impact of AI-based recruiting tools on the different subprocesses of recruiting
• The challenges and opportunities associated with the implementation of AI-based recruiting tools

Zusammenfassung der Kapitel (Chapter Summaries)

Chapter 1 introduces the research topic and provides a clear problem statement, outlining the objective and structure of the thesis. It discusses the increasing use of AI-based recruiting tools and highlights the need for a comprehensive analysis of their ethical implications.

Chapter 2 presents the theoretical background, delving into the concept of recruiting within the broader context of Human Resources. It explores the subprocesses of recruiting, including reaching out, preselecting, and assessing candidates. The chapter also provides a detailed explanation of machine learning and its application in AI-based recruiting tools, examining various supervised learning algorithms and relevant ethical principles.

Chapter 3 outlines the methodological approach employed in the literature review. It describes the process of defining the scope of the review, conceptualizing the topic, and conducting a systematic literature search. The chapter details the selection of relevant journals and conferences, search databases, keywords, and the forward and backward search strategy.

Chapter 4 presents the results of the literature review, analyzing the use of AI-based recruiting tools and their ethical implications. It examines the impact of AI-based tools on each subprocess of recruiting, highlighting both challenges and opportunities. The chapter further analyzes the addressing of fairness and transparency concerns arising from the use of AI-based recruiting tools.

Schlüsselwörter (Keywords)

This literature review focuses on the key concepts of Artificial Intelligence (AI), recruiting, ethical implications, fairness, transparency, and machine learning algorithms. It examines the use of AI-based recruiting tools, analyzing their impact on the subprocesses of recruiting, including reaching out, preselecting, and assessing candidates. Furthermore, it explores the challenges and opportunities associated with the implementation of AI-based recruiting tools, highlighting the crucial aspects of ethical considerations such as fairness and transparency.

• Université: Friedrich-Alexander University Erlangen-Nuremberg (Wirtschaftsinformatik)
• Note: 1,3
• Auteur: Matthias Rudolph (Auteur)
• Année de publication: 2020
• Pages: 130
• N° de catalogue: V978174
• ISBN (ebook): 9783346324214
• ISBN (Livre): 9783346324221
• Langue: anglais
• mots-clé: Artificial Intelligence, Explainable AI, Künstliche Intelligenz, Ethics in AI