Bias and Fairness in AI Decision Making for Hiring Processes

Artificial intelligence algorithms have been found to perpetuate societal biases that have been embedded in the system over decades due to historical discrimination. In the recruitment sector, this often manifested in biased hiring decisions, where the AI-driven hiring tools inherited patterns that led to unfair outcomes. The purpose of this research was to detect and mitigate such bias by providing transparent explanations for an AI-driven decision, which supports fairness in recruitment processes. A machine learning model was developed to assess candidate suitability based on multiple recruitment-related features. The SHAP (SHapley Additive exPlanations) framework was applied to interpret the model's predictions and to identify the influence of individual features, with particular attention to gender as a sensitive attribute. The purpose of using the XAI (explainable AI) technique was to identify and examine potential bias in order to mitigate it. Examination and analysis of sensitive variables behind an outcome, such as gender, revealed a measurable influence on model predictions. These findings helped identify potential biases in the recruitment model and informed modifications aimed to reduce their impact. Incorporating XAI-based transparency into recruitment algorithms provided a systematic and actionable approach to bias detection. This approach enhanced the fairness of the AI recruitment tools and fostered greater trust in their adoption.