Ruhr-Uni-Bochum

CVs Wanted for a Research Project on Discrimination in Job Application Procedures

Software systems based on algorithms can contribute to the discrimination of people applying for a jobs. To change that, support is needed!

Copyright: FINDHR

Copyright: FINDHR

Companies are increasingly using automated systems to help process job applications. These systems rely on algorithms. Many examples show that the implementation of automated systems can lead to unfair treatment of women, migrants, and other marginalized people. That means there is less chance of them getting a job compared to others. In other words, these algorithms can lead to discrimination on the job market.

The FINDHR Data Donation Campaign is working to detect and reduce such injustices with 13 partners organizations from across Europe in a Horizon Europe research project, in order to develop tools that reveal discrimination in job selection processes and create methods to avoid such discrimination. CASA Principal Investigator Asia J. Biega from the Max Planck Institute for Security and Privacy is also participating in the project.

To do so, the FINDHR-team needs real CVs and résumés to test how injustices creep into selection systems. Based on those real CVs the plan is to create artificial CVs with software and use them to develop anti-discrimination methods.

More information on how to donate your CV are here: https://findhr.eu/datadonation/

Information on their data protection procedures can be found here.

The participating institutions are: Universitat Pompeu Fabra (Barcelona), Universiteit van Amsterdam, Universitá di Pisa, Radboud Universiteit, Max Planck Institute for Security and Privacy, Erasmus Universiteit Rotterdam, Praksis, Randstad, Adevinta, Eticas, ETUC, WIDE+, and AlgorithmWatch CH. 

The project is funded by the European Union under the Horizon Europe funding program (101070212) and through the Swiss State Secretariat for Education, Research and Innovation. It runs from November 2022 to October 2025.

General note: In case of using gender-assigning attributes we include all those who consider themselves in this gender regardless of their own biological sex.