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Time to Question if We Should: Data-Driven and Algorithmic Tools in Public Employment Services

by Pieter Delobelle, Kristen M. Scott, Sonja Mei Wang, Milagros Miceli, David Hartmann, Tianling Yang, Elena Murasso, Karolina Sztandar-Sztanderska, Bettina Berendt

(International workshop on Fair, Effective And Sustainable Talent management using data science 2022)

Algorithmic and data-driven systems have been introduced to assist Public Employment Services (PES) in various countries. However , their deployment has been heavily criticized. This paper is based on a workshop organized by a distributed team of researchers in AI ethics and adjacent fields, which brought together academics, system developers , representatives from the public sector, civil-society organizations, and participants from industry. We report on the workshop and analyze three salient discussion topics, organized around our research questions: (1) the challenge of representing individuals with data, (2) the role of job counsellors and data-driven systems in PES, and (3) questions around the interactions between job seeker, counsellor, and system. Finally, we consider lessons learned from the workshop and describe plans aiming at involving a multiplicity of stakeholders in a co-design process.
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