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

Algorithmic Tools in Public Employment Services: Towards a Jobseeker-Centric Perspective
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公共就业服务中的算法工具:以求职者为中心的视角

DOI:
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发表时间:
2022
期刊:
Conference on Fairness, Accountability and Transparency
影响因子:
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通讯作者:
Bettina Berendt
Bettina Berendt
中科院分区:
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文献类型:
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作者:
Kristen M. Scott;Sonja Mei Wang;Milagros Miceli;Pieter Delobelle;Karolina Sztandar;Bettina Berendt

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数据驱动和算法系统已被引入,以支持世界各地的公共就业服务。这些系统的部署引起了公众的争议,因此,其中一些系统已停止使用或其作用被削弱。然而,类似制度的实施仍在继续。在本文中,我们使用参与式的方法来确定在这一领域的研究和发展的前进方向。我们提请注意直接受这些系统影响的人的需要和期望,即,求职者。我们的调查包括两个研讨会:第一个是与学者、系统开发人员、公共部门和民间社会组织的事实调查研讨会,第二个是与13名失业移民到德国的共同设计研讨会。根据实况调查研讨会的讨论,我们确定了现有PES(算法)系统的挑战。在联合设计研讨会上,我们确定了参与者在接触PES时的需求和愿望:人际接触的需求、获得真正方向的期望以及被视为一个完整的人的愿望。我们将这些期望映射到PES数据驱动和算法系统的三个设计考虑因素:人际互动的重要性,求职者评估的方向,以及减轻虚假陈述的挑战。最后,我们认为,目前的系统的局限性和风险不能通过微小的调整,但需要一个更根本的改变PES的作用。
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.