I finished the Project Implicit Age Implicit Association Test (IAT) at Harvard University for this project. According to my findings, there was a modest innate preference for younger people. Even among people who value justice and equal opportunity, the results show how deeply embedded cultural stereotypes about age and work performance may be, even though I did not knowingly support this.
The validity and dependability of hiring and selection procedures may be severely impacted by implicit age bias. When assessors use divergent standards based on age-related presumptions, reliability is diminished. For instance, even when credentials and experience are similar, younger applicants may be seen as more inventive or vivacious, whereas older applicants may be seen as less flexible or tech-savvy. These unintentional presumptions may lead to different candidates receiving different evaluations.
When recruiting decisions are based on age preconceptions rather than job-related criteria, validity is also jeopardized. The selection process no longer accurately predicts job performance when decision-makers prioritize perceptions of “fit,” career stage, or expected longevity over pertinent knowledge, skills, and talents. Reliance on unstructured interviews raises the possibility of bias since they prioritize intuition over objective assessment, as covered in the readings and course materials (Payne, Niemi, Doris, 2018). Standardized evaluation criteria and structured interviews are two powerful tools for combating age-related unconscious prejudice. Organizations can promote fairness and validity while decreasing subjective judgment by utilizing clear grading scales and asking all candidates the same job-related questions. Individually, being conscious of implicit age prejudice promotes more deliberate decision-making and helps reorient attention away from presumptions and toward evidence-based evaluations.
While addressing implicit prejudice does not completely eradicate it, it is a crucial first step in making recruiting decisions that are fairer, justifiable, and more efficient.
Sources:
Payne, K., Niemi, L., & Doris, J. M. (2018, March 27). How to think about implicit bias. Scientific American. https://www.scientificamerican.com/article/how-to-think-about-implicit-bias/
Project Implicit. Implicit Association Test. Harvard University. https://implicit.harvard.edu