Algorithmic Tools in Public Employment Services: Towards a Jobseeker-Centric Perspective

Abstract

Data-driven and algorithmic systems have been introduced to support Public Employment Services (PES) throughout the world. Their deployment has sparked public controversy and, as a consequence, some of these systems have been removed from use or their role was reduced. Yet the implementation of similar systems continues. In this paper, we use a participatory approach to determine a course forward for research and development in this area. We draw attention to the needs and expectations of people directly affected by these systems, i.e., jobseekers. Our investigation comprises two workshops: the first a fact-finding workshop with academics, system developers, the public sector, and civil-society organizations, the second a co-design workshop with 13 unemployed migrants to Germany. Based on the discussion in the fact-finding workshop we identified challenges of existing PES (algorithmic) systems. From the co-design workshop we identified our participants’ needs and desires when contacting PES: the need for human contact, the expectation to receive genuine orientation, and the desire to be seen as a whole human being. We map these expectations to three design considerations for data-driven and algorithmic systems for PES: the importance of interpersonal interaction, jobseeker assessment as direction, and the challenge of mitigating misrepresentation. Finally, we argue that the limitations and risks of current systems cannot be addressed through minor adjustments but require a more fundamental change to the role of PES.