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Research Paper Volume 7 Issue 2 2631 - 2652 April 17, 2024

A Critical Study on Artificial Intelligence and Ability to Reduce in Recruitment Bias in IT Companies, with special reference to Tamilnadu

Lead author · Corresponding
R Gohula Tharani
Student at Saveetha School of Law, Saveetha Institute of Medical And Technical Sciences (SIMATS), India
Abstract

This study delves into the realm of Artificial Intelligence (AI) and its transformative potential in mitigating recruitment bias within IT companies, focusing on the unique context of Tamil Nadu, India. The research critically analyses the existing biases prevalent in traditional recruitment processes and investigates how AI-driven tools and algorithms can serve as effective remedies. The study employs a comprehensive literature review to establish the theoretical foundation, exploring key concepts such as recruitment bias, AI technologies, and their ethical implications. Methodologically, a combination of qualitative and quantitative approaches is adopted, including surveys, interviews, and case studies conducted within various IT firms across Tamil Nadu. Through rigorous analysis, the research evaluates the impact of AI tools in minimising unconscious biases associated with gender, ethnicity, and other demographic factors. It investigates the implementation of AI-driven resume screening, chatbots, and predictive analytics in enhancing objectivity and fairness in the recruitment process. Moreover, the study examines the challenges and ethical dilemmas that emerge with the integration of AI, emphasising the importance of responsible AI development and deployment. The findings of this study contribute valuable insights to the fields of AI ethics, human resources, and IT industry practices.

Type
Research Paper
Information
International Journal of Law Management and Humanities, Volume 7, Issue 2, Page 2631 - 2652
Creative Commons
CC BY-NC 4.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution–NonCommercial 4.0 International (CC BY-NC 4.0) (https://creativecommons.org/licenses/by-nc/4.0/), which permits remixing, adapting, and building upon the work for non-commercial use, provided the original work is properly cited.
Copyright
Copyright © IJLMH 2026
Disclaimer
The views and opinions expressed in this manuscript are those of the author(s) alone and do not reflect the views, policies, or position of the Journal.

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