Year: 2024
Type: article
Abstract: This article explores the critical role of Explainable Artificial Intelligence (XAI) in mitigating bias within AI-driven recruitment processes. As AI becomes increasingly prevalent in hiring practices, concerns about algorithmic bias and fairness have emerged. The article discusses how XAI techniques, such as SHAP and LIME, can be used to detect and interpret potential biases in recruitment algorithms. It examines the implementation of XAI for feature importance analysis, algorithmic bias detection, and disparate impact analysis across different demographic groups. The article addresses the challenges of balancing model complexity with explainability and the limitations of XAI in identifying systemic biases. By implementing XAI strategies, organizations can enhance the fairness and transparency of their hiring practices, ultimately fostering more diverse and equitable workplaces.
Author: Ravi Kiran Magham
Institution: Osmania University
Language: English
Subfield: Safety Research
Field: Social Sciences
Domain: Social Sciences
SDG: Reduced inequalities
Open Access status: diamond
OpenAlex ID: W4403281560