AI in Recruitment: Addressing Bias, Ethical Issues, and Fair Hiring Practices

The increasing adoption of Artificial Intelligence (AI) in recruitment has transformed traditional hiring processes by enhancing efficiency, accuracy, and decision-making capabilities. However, the use of AI-driven recruitment systems has raised significant concerns regarding bias and fairness. This study examines how AI-powered recruitment tools may perpetuate bias due to factors such as historical data, lack of diversity in datasets, and algorithmic design. The research further explores the impact of such biases on fairness and diversity in hiring outcomes.

The study is based on primary data collected from 100 respondents using a structured questionnaire. Descriptive statistical tools such as percentage and mean score were used for analysis. The findings reveal that while AI significantly improves recruitment efficiency, it also contributes to bias in hiring decisions. The study confirms that bias in AI systems negatively affects fairness and diversity in recruitment processes. However, strategies such as algorithm auditing, human intervention, and implementation of ethical guidelines are found to be effective in reducing bias and improving fairness.

The study concludes that organizations must adopt a balanced approach by integrating AI with ethical practices and human oversight to ensure fair and transparent recruitment. The research contributes to the growing body of knowledge on responsible AI adoption in Human Resource Management and provides practical recommendations for minimizing bias in AI-driven hiring systems.