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EXPLORING THE IMPACT OF THE ALGORITHMS ON MANAGEMENT HIRING DECISION AND BIAS
Papagelis, Suela
Papagelis, Suela
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Thesis/Dissertation
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2024-05
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Business Administration/Human Resource Management
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http://dx.doi.org/10.34944/dspace/10144
Abstract
This research seeks to grasp the effect that trust in algorithm word has on the decisions made by hiring managers when it comes to selecting candidates. Specifically, this research will focus on whether trust in algorithms affects how much emphasis hiring managers put on important traits such as Experience, Education, and Qualifications. Ultimately, the goal of this research is to assess whether algorithm formulas or traditional assessment methods are currently producing better hires for organizations. Understanding the impact of trust in algorithms will help determine which method is best for employers to use moving forward. Also, how managers cope with bias and what role they play during the hiring selection. The data collected from the experiment will help identify and analyze Artificial Intelligence's impact on hiring managers' decisions. The study will also use the data from the experiment to test the hypothesis. Ultimately, this will help us to determine if Artificial Intelligence can reduce bias in the recruitment process and provide employers with more accurate insights into applicants' abilities. This study is expected to lead to more efficient and effective use of Artificial Intelligence in recruitment while helping employers make more informed decisions. We hope this research will pave the way for a more equitable hiring process by reducing bias and providing an objective evaluation of applicants' abilities. By having such a variety of diversity in Industries, Race and Gender, this research is a piece of real world that every employer can replicate for their hiring or training employees. We look forward to seeing how AI can improve the recruitment process. By accurately assessing applicants and considering their abilities, employers can make informed decisions that benefit both applicants and employers.
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