Journal of Public Administration

Journal of Public Administration

Strategies for Mitigating Cognitive Bias in the Application of Artificial Intelligence to Human Resource Recruitment and Selection

Document Type : Research Paper

Authors
Mohammad Bashokouh Ajirloo 1
Bahman Khodapanah 2
Mehri Maghami Dolatabad 3
1 Prof., Department of Business Management, University of Mohaghegh Ardabili, Ardabil, Iran.
2 Assistant Prof., Department of Business Management, University of Mohaghegh Ardabili, Ardabil, Iran.
3 PhD Candidate, Department of Business Management, University of Mohaghegh Ardabili, Ardabil, Iran.
10.22059/jipa.2026.417273.3944
Abstract
Objective
This study sought to identify and explain strategies for mitigating cognitive bias in the application of artificial intelligence (AI) to human resource recruitment and selection. As intelligent systems have become more prevalent in human resource management, algorithms are increasingly used to screen résumés, analyze competencies, rank candidates, and support hiring decisions. When properly designed and used, such systems can improve the speed, consistency, and traceability of decisions. Yet their reliance on historical data, proxy variables, ambiguous criteria, and biased human judgments carries the risk of reproducing or even intensifying existing inequalities. In addition, the cognitive biases of decision-makers, in selecting data, interpreting model outputs, and endorsing algorithmic recommendations, can undermine the fairness of the recruitment process. Accordingly, the central problem addressed by this study was to identify the human, data-related, algorithmic, and organizational mechanisms for reducing cognitive bias in AI-based recruitment.
Methods
The study adopted a qualitative approach and used thematic analysis. Data were gathered through 12 in-depth, semi-structured interviews with human resource experts, technology specialists, and individuals familiar with the design or use of intelligent systems in Iranian organizations. Participants were selected through purposive sampling, and interviews continued until theoretical saturation was reached. After the interviews were transcribed, the data were examined repeatedly to identify meaningful units related to the sources, manifestations, and strategies for controlling bias. The analysis proceeded through initial coding, extraction of basic themes, comparison and consolidation of similar codes, organization of themes, and formation of overarching (global) themes, and was carried out using MAXQDA version 20. To strengthen the credibility of the analysis, codes were reviewed continuously, raw data were revisited, participants’ perspectives were compared, and coherence between themes was examined. This process ultimately yielded 130 basic themes, 18 organizing themes, and 6 global themes.
Results
The final thematic structure indicated that strategies for mitigating cognitive bias in intelligent recruitment can be explained along six main dimensions: integrating interactive human–system decision-making; standardizing human-centered assessment processes; ensuring the transparency and explainability of intelligent systems; algorithmic monitoring and validation; identifying and quantitatively reducing data bias; and establishing ethical and operational protocols for intelligent systems. The findings indicate that human intervention reduces bias only when the human role extends beyond the formal approval of algorithmic recommendations to include critical evaluation of outputs, the possibility of reviewing decisions, documented justification for accepting or rejecting recommendations, and ultimate accountability. Standardizing competency indicators, training and cognitively retraining evaluators, ensuring the quality and representativeness of recruitment data, assessing differences in outcomes across applicant groups, continuously monitoring model performance, and conducting independent audits were likewise recognized as effective measures. Explainability of the system’s decision logic and the provision of mechanisms for applicant appeals also reinforce the trustworthiness and accountability of the process.
Conclusion
Bias in AI-based recruitment and selection is a multidimensional phenomenon that arises from the entanglement of humans, data, algorithms, and organizational structures. Its management, therefore, cannot be achieved through technical model refinement alone or by simply removing sensitive variables; rather, it calls for a sociomaterial approach that analyzes humans and technology within an interactive, decision-making network. Establishing meaningful human oversight, standardizing judgments, improving data quality, ensuring system transparency and validation, conducting periodic audits, and developing ethical and operational protocols can enhance recruitment fairness, applicant trust, and organizational accountability, thereby laying the groundwork for responsible AI governance in Iranian human resource management.
Keywords
Cognitive bias
Digital human resource management
Human-AI interaction
Intelligent recruitment
Subjects
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Journal of Public Administration
Volume 18, Issue 3
2026
Pages 697-725