Symbiosis Centre for Management and Human Resource Development, Pune, Maharashtra, India.
Abstract
Purpose: Technology has significantly impacted the workplace’s hiring practices in terms of diversity, objectivity, and efficacy. With the development of artificial intelligence and machine learning, companies now have access to cutting-edge solutions that improve diversity in the hiring process and eradicate unconscious prejudices.
Without taking into account a candidate’s gender, ethnicity, or other personal qualities, AI and ML may evaluate resumes, applications, and other data sources to find trends and forecast successful prospects. In this research, we will be able to analyze how significantly technology is impacting the hiring processes in terms of – diversity, efficiency and biasness.
Research Methodology: A primary research into the topics allows to find the basic understanding as to whether technology made any impact on hiring processes in regards to diversity hiring and unbiased hiring thereby leading to hiring effectiveness.
Findings: In accordance with the findings, technology has significantly influenced diversity recruiting strategies as organizations use tools and platforms to recruit and assess a larger pool of applicants. The most common technologies utilized to improve the diversity recruiting process were identified as automated resume screening, AI-driven applicant matching, and video interviewing platforms. Additionally, technology has made it possible to eliminate prejudice by standardizing evaluation standards, permitting blind evaluations, and anonymizing application information.
Value: This study serves a purpose because it offers insightful information
on how technology affects recruiting for diversity, hiring that is fair, and
hiring effectiveness. Organizations can decide to deploy and optimize
technological solutions in their recruiting practices by having a clear
awareness of the advantages and difficulties of technology adoption. The
findings emphasize the necessity for constant monitoring, review, and
improvement of these technologies to maintain justice, diversity, and
efficiency in the employment process.
Keywords: Diversity Hiring, Unbiased Hiring, Cost and Time Effectiveness, Hire Quality, Artificial Intelligence, Machine Learning
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