Synthetic CVs To Build and Test Fairness-Aware Hiring Tools
This addresses the problem of evaluating fairness techniques in hiring algorithms for researchers, but it is incremental as it provides a new dataset rather than a novel method.
The paper tackled the lack of datasets for studying bias in algorithmic hiring by introducing a synthetic dataset of 1,730 CVs modeled on real data, intended as a benchmarking standard for fairness research.
Algorithmic hiring has become increasingly necessary in some sectors as it promises to deal with hundreds or even thousands of applicants. At the heart of these systems are algorithms designed to retrieve and rank candidate profiles, which are usually represented by Curricula Vitae (CVs). Research has shown, however, that such technologies can inadvertently introduce bias, leading to discrimination based on factors such as candidates' age, gender, or national origin. Developing methods to measure, mitigate, and explain bias in algorithmic hiring, as well as to evaluate and compare fairness techniques before deployment, requires sets of CVs that reflect the characteristics of people from diverse backgrounds. However, datasets of these characteristics that can be used to conduct this research do not exist. To address this limitation, this paper introduces an approach for building a synthetic dataset of CVs with features modeled on real materials collected through a data donation campaign. Additionally, the resulting dataset of 1,730 CVs is presented, which we envision as a potential benchmarking standard for research on algorithmic hiring discrimination.