This master’s thesis explores the role of artificial intelligence (AI) in modern recruitment processes with a particular focus on applicants’ trust. As digitalization and AI increasingly transform the world of work, organizations are adopting AI-supported recruiting tools to enhance efficiency, standardization, and data-driven decision-making. At the same time, the use of AI in personnel selection raises important questions regarding transparency, fairness, and acceptance from the applicants’ perspective.
The objective of this thesis is to examine how applicants perceive AI-assisted recruitment processes and which aspects are relevant for the development of trust in this context. To address this research objective, a qualitative research design was chosen. Semi-structured interviews were conducted with participants who had either experienced AI-supported recruitment processes or reflected on their expectations toward such processes.
The empirical data were analyzed using qualitative content analysis following Kuckartz’ approach. This method allows for a systematic examination of subjective perceptions, attitudes, and evaluations, while combining theory-driven and data-driven coding. The analysis focuses on applicants’ experiences with different forms of AI usage in recruitment, their expectations toward human and technological decision-making, and their assessment of AI-supported processes within the broader candidate journey.
By linking theoretical concepts of trust in technology with empirical insights from applicants, this thesis contributes to the growing body of literature on AI in recruiting. In addition, it provides a structured foundation for further academic research and offers valuable insights for organizations seeking to better understand applicant perspectives on AI-supported recruitment processes.
Table of Contents
1 Introduction
1.1 Initial situation
1.2 Problem statement
1.3 Objectives and approach
1.4 Methodology
2 Theoretical background
2.1 Artificial Intelligence in recruiting
2.1.1 Definition of AI
2.1.2 Types of AI-tools in recruiting
2.1.3 Advantages of AI in recruiting
2.1.3.1 Efficiency
2.1.3.2 Objectivity
2.1.4 Challenges of AI in recruiting
2.1.4.1 Bias and discrimination risk
2.1.4.2 Lack of human interaction and judgement
2.1.4.3 Ethical and legal concerns
2.1.4.4 Lack of transparency
2.1.4.5 Technical limitations
2.1.5 Applicant perceptions of AI in recruiting
2.1.5.1 Emotional impacts
2.1.5.2 Preferences for human vs. AI-driven decisions
2.2 Trust in technology
2.2.1 Prerequisite for trust - procedural fairness
2.2.2 Definition of trust in AI
2.2.3 Initial trust building
2.2.3.1 Performance
2.2.3.2 Process
2.2.4 Ongoing trust development
2.2.4.1 Performance
2.2.4.2 Purpose
2.2.5 Factors influencing trust in AI
2.2.6 Strategies for trust building
2.3 Employer branding and candidate experience
2.3.1 The role of the recruiting process on employer branding
2.3.2 Impact of AI on employer branding
3 Research design and methodology
3.1 Research design
3.1.1 Rationale for choosing a qualitative approach
3.1.2 Limitations of the methodology
3.2 Data collection
3.2.1 Development of the interview guide
3.2.2 Pilot testing interview questions
3.2.3 Process of conducting semi-structured interviews
3.3 Participant selection
3.3.1 Inclusion criteria for participants
3.3.2 Recruiting process for applicants
3.4 Data analysis
3.4.1 Steps in qualitative content analysis (Kuckartz)
3.4.2 Coding and categorization of themes
3.5 Ethical considerations
3.5.1 Informed consent and voluntary participation
3.5.2 Anonymity and data protection measures
4 Results
4.1 Experiences with AI in recruiting
4.2 Perception of AI in recruiting
4.2.1 Positive
4.2.2 Negative
4.2.3 Fair
4.2.4 Unfair
4.2.5 Different forms of AI
4.3 Emotional response
4.4 Data protection concerns
4.5 Trust in AI based recruitment
4.5.1 Mistrust
4.5.2 Trust
4.5.3 Ambivalence
4.5.4 Compliance without trust
4.6 Transparency about AI usage
4.7 Adjustments in behavior
4.8 Effects on employer brand
4.9 AI recommendatory vs. autonomous
4.10 Trust enhancing factor - hybrid process
5 Discussion
5.1 Interpretation of findings
5.1.1 Alignment with existing literature
5.1.2 New insights and contributions
5.2 Practical implication
5.2.1 Human-AI collaboration
5.2.2 Communication
5.2.3 Managing emotional responses
5.2.4 Tailoring AI use to recruitment stages
5.2.5 Employer brand considerations
5.3 Limitations of the study
5.3.1 Methodological constraints
5.3.2 Generalizability of findings
5.4 Suggestions for future research
6 Conclusion
Objectives & Core Themes
The primary aim of this master thesis is to identify the specific factors that influence and foster applicants' trust in AI-powered recruitment processes. The research addresses the apparent tension between the increased efficiency of AI and the skepticism it often evokes among candidates, with a goal to provide practical recommendations for organizations to design trust-building recruitment strategies.
- Factors influencing candidate trust in AI systems during recruitment.
- The impact of transparency and communication on candidate behavior.
- The role of "hybrid" recruitment (human-AI collaboration) in maintaining trust.
- The effects of AI integration on perceived employer branding.
- Strategies to mitigate negative emotional responses like anxiety and dehumanization.
Excerpt from the Thesis
2.1.4.1 Bias and discrimination risk
The use of AI can raise ethical, legal, privacy, and moral concerns for job applicants (Van Esch et al., 2019, p. 220). One of the primary concerns is bias in AI decision-making (Lee, 2018, p. 10; Srivastava, 2025; Armstrong/Metaxa, 2025, p. 2; Fernández-Martínez/Fernández, 2020, p. 204; Mülder, 2021, p. 68). While AI can reduce bias by focusing on objective candidate qualifications, it may also reinforce existing biases or violate anti-discrimination laws leading to unethical hiring practices if trained on historically biased hiring data (Srivastava, 2025; Armstrong/Metaxa, 2025, p. 2; Fernández-Martínez/Fernández, 2020, p. 204; Mülder, 2021, p. 68). A practical example is Amazon’s AI recruiting tool, which systematically downgraded applications that included the word “women” because it had been trained on past hiring data in which most recruits were male (Fernández-Martínez/Fernández, 2020, p. 204; Mülder, 2021, p. 68). This highlights the risk that AI has the potential to factor in a candidate’s physical attributes when making hiring decisions (Van Esch et al., 2019, p. 220). Also, AI can unintentionally perpetuate discrimination, underscoring the importance of transparent algorithms and unbiased training data (Mülder, 2021, p. 68).
As AI becomes more integrated into recruiting, organizations must address challenges such as selection bias, delayed feedback, and technical issues. If these concerns are not managed, job seekers may remain dissatisfied, potentially harming an employer’s ability to attract and retain top talent (Van Esch et al., 2019, p. 220 and the sources cited there). Bias in AI models should be addressed by incorporating mitigation techniques, including the use of diverse training datasets. In addition, fairness checks should be conducted on a regular basis to ensure that unbiased hiring decisions are made (Srivastava, 2025).
Summary of Chapters
1 Introduction: Provides an overview of the rising importance of AI in recruitment, the problem of candidate mistrust, and the methodological framework used for this study.
2 Theoretical background: Establishes a foundation by defining AI tools in recruiting, exploring trust in technology, and analyzing the impact of AI on employer branding.
3 Research design and methodology: Details the qualitative research approach, specifically the use of semi-structured interviews and the Kuckartz approach for content analysis.
4 Results: Presents the findings from the interviews, covering topics like candidate experiences, perceptions of fairness, emotional responses, and the importance of a hybrid human-AI process.
5 Discussion: Critically evaluates the findings against existing literature, offering practical implications for organizations and acknowledging the study's limitations.
6 Conclusion: Synthesizes the core findings, confirming the preference for hybrid recruitment and highlighting the necessity of transparency and human oversight to foster trust.
Keywords
Artificial Intelligence, AI in Recruiting, Candidate Trust, Employer Branding, Procedural Fairness, Transparency, Hybrid Recruitment, Human-AI Collaboration, Algorithmic Bias, Data Protection, Qualitative Research, Recruitment Processes, Applicant Perception, Technology Adoption, Trust Building
Frequently Asked Questions
What is the core focus of this thesis?
This thesis examines the factors that influence and foster candidate trust when artificial intelligence is utilized within the recruitment and hiring process.
What are the primary themes addressed in this work?
The work explores AI's advantages and challenges in recruiting, the psychology of trust in automated systems, the impact on employer branding, and the necessity of transparent communication.
What is the research question driving this study?
The central research question is: "Which factors foster applicants' trust in AI-powered recruiting processes?"
What methodology was employed to gather data?
A qualitative approach was used, specifically conducting semi-structured interviews with seven participants who had varying degrees of experience with AI in recruitment.
What is covered in the main body of the thesis?
The main body integrates theoretical frameworks on trust and technology with empirical results obtained from participant interviews, leading to a practical discussion on how companies can improve their AI processes.
Which keywords characterize this research?
Key terms include AI in recruiting, trust, procedural fairness, employer branding, transparency, and the hybrid human-AI process.
Why do candidates often mistrust AI in recruitment?
Candidates express mistrust due to concerns about bias, the "black box" nature of algorithms, the lack of human interaction, and fears of dehumanization during the application process.
What is the role of the "hybrid process" discussed in the findings?
The hybrid process, which combines AI efficiency with human oversight, was identified as the most effective trust-building mechanism to mitigate fears of errors and dehumanization.
How does AI affect employer branding according to the study?
AI adoption can signal modernity and innovation, but if implemented poorly or without transparency, it may also lead to a perception of the firm as cold, impersonal, and unappreciative.
- Arbeit zitieren
- Ajla Besirevic (Autor:in), 2025, Artificial intelligence in the recruitment process. How can companies gain applicants’ trust?, München, GRIN Verlag, https://www.grin.com/document/1691797
