# Synthetic CVs To Build and Test Fairness-Aware Hiring Tools

## TL;DR

This paper presents a method for creating a synthetic CV dataset to evaluate and improve fairness in algorithmic hiring systems, addressing the lack of diverse, real-world data for bias analysis.

## Contribution

It introduces a novel approach for generating a synthetic CV dataset based on real data, providing a benchmarking resource for fairness research in hiring algorithms.

## Key findings

- Created a dataset of 1,730 synthetic CVs reflecting diverse backgrounds
- Demonstrated the dataset's potential for benchmarking fairness techniques
- Facilitated research on bias mitigation in algorithmic hiring

## Abstract

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.