Exploring Intersectional Fairness and Bias Mitigation Techniques in Job Recommendation

Along with the increased use of automation processes in every other task, job recommendations as well as hiring have also become partially automated. In the process of applying only to those jobs that are recommended by a system or choosing only from those candidates that are selected by an automated system, it becomes highly important to find out if the automated systems are trustworthy enough to provide fair decisions. There has been research on fairness in this sector, where the main focus has been on a single protected attribute, in most cases ’gender.’ That is why the aim of this research is to delve deeper into a deep learning transformer-based algorithm used for job matching with a text-based resume dataset containing several demographic attributes to investigate the fairness of the algorithm not only for gender but also for other demographic groups such as race, age group, and experience level. The fairness evaluation has been carried out using multiple fairness metrics, including demographic parity, conditional demographic parity, and equal opportunity. The transformer models that are pretrained language models are chosen for this study due to their ability to understand the meaning and context of words in resumes and job descriptions. The thesis further investigates bias using an alternative approach by working on a dataset containing varying protected attributes and then conducting a comparable analysis of several bias mitigation methods, including multiple layers of data resampling, along with sensitivity testing through data modification in the preprocessing step of the recommendation process. The research reveals that a system performing fairly when considering a single protected attribute can even hide intersectional unfairness and that the bias mitigation methods do not ensure a balanced improvement across each subgroup when considering multiple combinational demographic groups.