Location
Innovation Lab Atrium
Start Date
4-3-2025 10:00 AM
End Date
4-3-2025 11:30 AM
Presentation Date
3 April 2025, 10:00am - 11:30am
Biography
Eyuel Getahun is a doctoral student in Engineering Management at Missouri University of Science and Technology, where he also completed his M.S. in Engineering Management in 2024. His research examines the interplay between human and algorithmic biases and their influence on hiring decisions.
Meeting Name
2025 - Miners Solving for Tomorrow Research Conference
Department(s)
Engineering Management and Systems Engineering
Second Department
Psychological Science
Document Type
Poster
Document Version
Final Version
File Type
event
Language(s)
English
Rights
© 2025 The Authors, All rights reserved
Included in
Operations Research, Systems Engineering and Industrial Engineering Commons, Social Psychology Commons
How Do Human and AI Gender Bias Interact in Hiring Decisions?
Innovation Lab Atrium
Comments
Advisor: Casey I. Canfield
Abstract:
The hiring process is critical for organizational success but often reflects gender bias, particularly favoring men in traditionally masculine roles. Many organizations now use Artificial Intelligence (AI) to reduce such bias, yet AI can also carry its own biases. This study explores how human and algorithmic biases interact to shape hiring outcomes. In an online experiment with 600 participants acting as hiring managers, we examined evaluations of a candidate for a Chief Electrical Engineer position, using a 3 (AI recommendation: no AI, elevated AI, depressed AI) × 2 (candidate gender: male, female) design. Results showed that AI recommendations significantly influenced competence and likability ratings. However, elevated AI scores benefited male candidates more than female candidates. Participants’ gendered attitudes also impacted evaluations, with higher sexism scores linked to lower ratings for female candidates. These findings highlight the importance of addressing both human and AI biases to ensure equitable recruitment practices.