THE ANALYSIS OF BIASES AND ITS EFFECTS IN AI-SUPPORTED HUMAN RESOURCES DECISION MAKING

The use of Artificial Intelligence is on the rise in several human resources functions such as recruitment and hiring, employee records management, payroll processing and benefits administration, performance management, employee onboarding offboarding processes, training. However, although the AI im...

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Bibliographic Details
Main Author: Lacmanovic, Sabina
Format: Conference Proceeding
Language:English
Subjects:
Artificial intelligence
Bias
Cognitive ability
Compensation
Decision analysis
Decision making
Efficiency
Employee involvement
Ethics
Human resource management
Human resources
Influence
Literature reviews
Mathematical analysis
Performance management
Privacy
Professional ethics
Qualitative research
Records management
Training
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Summary:The use of Artificial Intelligence is on the rise in several human resources functions such as recruitment and hiring, employee records management, payroll processing and benefits administration, performance management, employee onboarding offboarding processes, training. However, although the AI improves the HR efficiency, the ethical considerations of its use and the biases which humans transfer to AI systems are still to be thoroughly examined. The purpose of the paper is to explore the significance of biases in AI systems and its effects on the human resources related decisions. The paper's aim is to analyze how human cognitive biases influence and misdirect AI systems used in human resources decision making processes. The author conducted a qualitative research based on the systematic theme-relevant literature review which resulted in the following findings: there are several sources of biases in AI systems which should be addressed to mitigate eliminate the prejudices-led AI-supported discriminatory HR decisions. The author also analyzes the possible conflict between analytically efficient AI-assisted decision making and the value-based moral human judgment which implicates that decision makers should be aware of possible AI 's built-in biases, but also to always bring in the ethical issues in AI-supported decision-making process, to prevent the unmoral decisions. The paper presents possible solutions for managerial and human resources practice to reduce biases in decision-making process which could result in higher individual and organizational performance. Further research can be conducted in various AI-supported business sectors and organization departments to help improve decision making processes and through that to improve the organization 's effectiveness.1
ISSN:1849-6903
1849-6903