Abstract

This dataset contains information about the hiring process conducted by Akkodis for job positions and candidates belonging to the STEM field. The data of the candidates have been thoroughly anonymized. The data contains the curricula of candidates and the details of the job positions to which they were matched. The candidates and job positions are explicitly selected among the STEM field (including but not limited: computer science, engineering, physics, mathematics, chemistry, etc.). More detailed information can be found in the README file included in the compressed archive. The goal of the data is to allow the study of bias in terms of: 1) class imbalanced dataset in favour of the election of a specific gender, age, race, social background, thus against a selected group of people or minorities; 2) interpretation (recruiter) bias. This activity is part of the HORIZON-CL4-2021-HUMAN-01-24-AEQUITAS project (g.a. 101070363). The aim of AEQUITAS to address and tackle the multiple manifestations of bias and unfairness in Artificial Intelligence (AI) from a variety of dimensions, such as the development of AI tools, the data used to train, test and validate them or the interpretation practices developed around them.