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<p>Artificial Intelligence (AI) is transforming recruitment by allowing organisations to employ data-driven hiring decisions. However, AI-powered tools are prone to reinforcing biases instead of eliminating them, posing ethical and fairness concerns. Since AI-powered tools are critical in hiring, this study introduces HITHIRE, an AI-driven recruitment model designed to enhance transparency, fairness, and inclusivity within the diverse workforce of Saudi Arabia, which aligns with Vision 2030. A baseline model was first evaluated using Llama 3.1, BERT, and regular NPL techniques. However, the baseline model revealed significant biases in gender and nationality-based hiring, making it inappropriate for the Saudi Arabian diverse hiring environment. The Llama 3.1 model was enhanced through data augmentation, sentence transformers, standard scoring, and transparency mechanisms, resulting in the HITHIRE model. Fairness analysis demonstrated improvements across gender and nationality dimensions, with reduced Statistical Parity Difference (SPD) and Disparate Impact (DI) scores. The findings highlight the potential of ethical AI integration in recruitment, ensuring unbiased, accountable, and transparent hiring practices. HITHIRE sets a precedent for AI-driven fairness in recruitment, contributing to HR policies and ethical AI discourse globally.</p>

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Addressing intersectional bias in AI recruitment using HITHIRE model: a fair, ethical, green AI and transparent hiring solution for Saudi Arabia’s diverse workforce in line with vision 2030

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月11日

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  • Elham Albaroudi,
  • Taha Mansouri,
  • Mohammad Hatamleh,
  • Ali Alameer

摘要

Artificial Intelligence (AI) is transforming recruitment by allowing organisations to employ data-driven hiring decisions. However, AI-powered tools are prone to reinforcing biases instead of eliminating them, posing ethical and fairness concerns. Since AI-powered tools are critical in hiring, this study introduces HITHIRE, an AI-driven recruitment model designed to enhance transparency, fairness, and inclusivity within the diverse workforce of Saudi Arabia, which aligns with Vision 2030. A baseline model was first evaluated using Llama 3.1, BERT, and regular NPL techniques. However, the baseline model revealed significant biases in gender and nationality-based hiring, making it inappropriate for the Saudi Arabian diverse hiring environment. The Llama 3.1 model was enhanced through data augmentation, sentence transformers, standard scoring, and transparency mechanisms, resulting in the HITHIRE model. Fairness analysis demonstrated improvements across gender and nationality dimensions, with reduced Statistical Parity Difference (SPD) and Disparate Impact (DI) scores. The findings highlight the potential of ethical AI integration in recruitment, ensuring unbiased, accountable, and transparent hiring practices. HITHIRE sets a precedent for AI-driven fairness in recruitment, contributing to HR policies and ethical AI discourse globally.