Mitigating Biases and Stereotypes in the Recruitment Process through Artificial Intelligence

Ing. Adriána Krétová

absolventská práce studenta CŽV

Abstract:

Organizations increasingly recognize the crucial role of diversity, equity, and inclusion and are actively incorporating these strategies into their company cultures. It's vital to attract, retain, and nurture a diverse pool of talent to foster innovation and achieve organizational success. Biased decision-making in the hiring process significantly impacts workplaces, often leading to homogeneous environments that hinder the inclusion of individuals from different backgrounds. Well-developed artificial intelligence applications have the potential to significantly transform the hiring process by reducing biases and stereotypes deeply rooted in traditional methods. Their ability to impartially analyse data and identify patterns without human biases introduces a new paradigm that supports fairness and inclusion in hiring.

Abstract:

Organizations increasingly recognize the crucial role of diversity, equity, and inclusion and are actively incorporating these strategies into their company cultures. It's vital to attract, retain, and nurture a diverse pool of talent to foster innovation and achieve organizational success. Biased decision-making in the hiring process significantly impacts workplaces, often leading to homogeneous environments that hinder the inclusion of individuals from different backgrounds. Well-developed artificial intelligence applications have the potential to significantly transform the hiring process by reducing biases and stereotypes deeply rooted in traditional methods. Their ability to impartially analyse data and identify patterns without human biases introduces a new paradigm that supports fairness and inclusion in hiring.

Keywords

artificial intelligence, biases, recruitment process, stereotypes

Keywords

artificial intelligence, biases, recruitment process, stereotypes

Language used: English

Date on which the thesis was submitted / produced: 27. 5. 2024

Thesis defence

• Supervisor: Ing. Eva Isabelle Křeček
• Reader: Karel Kohout, externi

ŠKODA AUTO a.s.

Škoda Auto Vysoká škola o.p.s.

celoživotní:
Master of Business Administration