Navigating the Maze: Biased Hiring and Its Implications on Projected Employee Performance

EasyChair Preprint 11496

Abstract

This research explores the intricate relationship between biased recruitment practices and predicted performance in the contemporary hiring landscape. Biased recruitment, encompassing intentional and unintentional actions that unfairly disadvantage certain candidate groups, has profound implications for organizational diversity, equity, and inclusion. Predicted performance, leveraging data and analytics to assess a candidate's likelihood of success, emerges as a potential remedy to mitigate the impact of biased practices. This study delves into the multifaceted dimensions of biased recruitment and investigates the efficacy of predicted performance in enhancing hiring accuracy and promoting fair and equitable hiring processes. The findings shed light on the interconnected dynamics of these two critical aspects of talent acquisition and offer insights into strategies for fostering diversity and improving organizational performance.

Keyphrases: Biased recruitment, Predicted performance, diversity

@booklet{EasyChair:11496,
  author    = {Lee Kasowaki and Oman Aryan},
  title     = {Navigating the Maze: Biased Hiring and Its Implications on Projected Employee Performance},
  howpublished = {EasyChair Preprint 11496},
  year      = {EasyChair, 2023}}