Explainable AI in employment decision-making: A systematic review of transparency methods in hiring algorithms

Artificial intelligence (AI) tools have increasingly been shaping employment decisions, from resume screening to employment tests and automated video interviews. Concerns about the "black box" nature of these tools have grown as many of these algorithmic models offer little insight into how or why hiring decisions occur. This lack of transparency undermines fairness, accountability, and regulatory compliance, especially in contexts where bias may persist or worsen. Explainable AI (XAI) is emerging as a critical strategy to address these concerns by providing interpretable outputs that reveal the model's logic and decision-making factors. This study presents a systematic review of explainable artificial intelligence (XAI) techniques utilized in hiring and employment-related AI systems. It identifies prominent methods, such as Shapley Additive Explanations (SHAP), Local Interpretable Model-agnostic Explanations (LIME), decision trees, and counterfactuals, and evaluates their performance in terms of interpretability, fidelity, and fairness. Additionally, it examines their impact on candidate trust and organizational transparency. The findings suggest while XAI can improve understanding and reduce biases, technical complexity and human interpretability issues often limit its effectiveness. The paper concludes by recommending a human-centred approach to XAI design and the development of stronger regulatory frameworks for fair and equitable hiring practices.