Predictive Modeling on Real Hiring Data: Exploring Salary Stereotypes and Bias Mitigation

This project focuses on developing a predictive model using real-world hiring data from AKKODIS to investigate and address salary-related stereotypes and potential biases in recruitment processes. The objective is to analyze historical hiring data to identify patterns that may indicate bias in salary offers (RAL) and to develop models that promote fairness and transparency. By leveraging machine-learning techniques, we aim to detect underlying trends that may not be immediately apparent through traditional analysis. The findings of this study can help organizations implement data-driven policies to mitigate bias and ensure an equitable distribution of salaries.