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- Gender Discrimination in Hiring: Evidence from a Cross-National . . .
By utilizing data from the first harmonized comparative field experiment on gender discrimination in hiring in six countries, we can directly compare employers’ callbacks to fictitious male and female applicants
- Gender equity in hiring: examining the effectiveness of a personality . . .
Introduction: Gender biases in hiring decisions remain an issue in the workplace Also, current gender balancing techniques are scientifically poorly supported and lead to undesirable results, sometimes even contributing to activating stereotypes
- Gender and Racial Bias in Hiring
First, empirical evidence, which we review below, suggests that biased evaluations of women and minorities contribute to their under representation among American faculty
- Bias in Context: Small Biases in Hiring Evaluations Have Big Consequences
cused on advancing practical strategies or techniques managers can use directly identify and reduce the influence of bias on hiring outcomes Instead, research has focused more on indirect tactics such as proactively seeking out qualified minorities to fill out applicant pools using targeted recruitment (Avery McKay, 2006; Avery, McKay
- Gender discrimination in hiring: Intersectional effects with ethnicity . . .
Building on a multiple categorization and cognitive matching perspective, this study investigated how applicants’ gender intersects with other status characteristics (ethnicity) and cognitive job demands for a better understanding of gender discrimination in resumé screening
- Getting a Foot in the Door: A Meta-Analysis of U. S. Audit Studies of . . .
First, the gender composition of an occupation predicts gender bias in hiring Second, the intersection of gender and race is critical—in female-dominated jobs, White female applicants receive more callbacks than their male counterparts, but Black female applicants experience no such benefit
- Gender Bias in the Job Market: A Longitudinal Analysis
To answer these questions, we perform a study with two components, an empirical, data-driven component that quantifies the presence and magnitude of gender bias in job postings over the last 10 years, and a qualitative user-study that seeks to understand the end-to-end impact of biases on whether applicants apply to a posted position
- (PDF) Gender Bias in AI Recruitment Systems: A Sociological-and Data . . .
The key findings of this study are threefold: identifying potential sources of human bias from a recruitment panel's ranking of CVs; identifying sources of bias from a potential algorithmic pipeline which simulates human decision making; and recommending ways to mitigate bias from both aspects
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