Crosscurrents: Welfare

Kaun, Anne; Lomborg, Stine; Pentzold, Christian; Alhutter, Doris; Sztandar‐Sztanderska, Karolina

doi:10.1177/01634437231154777

Cited by 7 publications

(2 citation statements)

References 23 publications

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“…However, the difference between automation for decision support, where a human has the final say, and fully automated decision-making (with no human intervention) can be crucial, not least because studies on the type of automation in the public sector that people generally trust show that support is far greater for ADM as a non-fully autonomous decision-support mechanism, where a human makes the final decision, than as a fully automated practice (Insight Intelligence, 2022). This type of literature often points to the significance and consequences of the development towards the quantification, or so-called datafication, of a range of processes, such as in welfare (Kaun et al, 2023) or in quantifying work and workers (Ajunwa, 2023).…”

Section: Definitional Difficultiesmentioning

confidence: 99%

The Artificial Recruiter: Risks of Discrimination in Employers’ Use of AI and Automated Decision‐Making

Larsson,

White,

Ingram Bogusz

2024

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Extant literature points to how the risk of discrimination is intrinsic to AI systems owing to the dependence on training data and the difficulty of post hoc algorithmic auditing. Transparency and auditability limitations are problematic both for companies’ prevention efforts and for government oversight, both in terms of how artificial intelligence (AI) systems function and how large‐scale digital platforms support recruitment processes. This article explores the risks and users’ understandings of discrimination when using AI and automated decision‐making (ADM) in worker recruitment. We rely on data in the form of 110 completed questionnaires with representatives from 10 of the 50 largest recruitment agencies in Sweden and representatives from 100 Swedish companies with more than 100 employees (“major employers”). In this study, we made use of an open definition of AI to accommodate differences in knowledge and opinion around how AI and ADM are understood by the respondents. The study shows a significant difference between direct and indirect AI and ADM use, which has implications for recruiters’ awareness of the potential for bias or discrimination in recruitment. All of those surveyed made use of large digital platforms like Facebook and LinkedIn for their recruitment, leading to concerns around transparency and accountability—not least because most respondents did not explicitly consider this to be AI or ADM use. We discuss the implications of direct and indirect use in recruitment in Sweden, primarily in terms of transparency and the allocation of accountability for bias and discrimination during recruitment processes.

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Section: Definitional Difficultiesmentioning

confidence: 99%

The Artificial Recruiter: Risks of Discrimination in Employers’ Use of AI and Automated Decision‐Making

Larsson,

White,

Ingram Bogusz

2024

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“…Denmark, along with its Nordic neighbors, is considered a model for highly digitalized and datafied welfare states (Andreassen et al, 2021). The burgeoning automation in public welfare services depends on the pervasive datafication of citizens across domains of everyday life (Kaun et al, 2023). While opting out of commercial digital and datafying media is arguably possible, if cumbersome (Portwood-Stacer, 2013), citizens’ opting out of a (datafied) relationship with the welfare state may be far more difficult.…”

Section: Digital Resignation In the Commercial And Commodified Arenamentioning

confidence: 99%

Digital resignation and the datafied welfare state

Bagger,

Einarsson,

Andelsman Alvarez

et al. 2023

Big Data & Society

Self Cite

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This commentary calls for further research into digital resignation within non-market contexts, particularly in relation to the datafied welfare state, as distinct from commercial big tech platforms. We aim to nuance the concept of digital resignation by relating it to the digitization of institutions and public services upholding the Danish welfare state, including health services, childcare, and news consumption. These cases illustrate that datafication stimulates citizens’ discomfort by registering privacy-intrusive information and setting new standards for being a good citizen, which resignation research can help us understand. We use the case examples to propose new avenues for digital resignation research and question whether organizations, institutions, and governments themselves can be digitally resigned. As such, the usefulness of digital resignation as a concept can be expanded.

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Rethinking keywords in media and cultural studies during and beyond COVID-19: Editorial

Natale,

Li,

Punathambekar

et al. 2024

Media, Culture & Society

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A few years after the COVID-19 pandemic swept across the globe, discussions surrounding its impact have become noticeably less frequent within communication and media research. While the pandemic no longer occupies the central place it once held in academic discourse and public debate, it is now more crucial than ever to consider how this unprecedented global event has shaped our societies, as well as its lasting implications for communication and media worldwide. This Crosscurrents themed issue invited scholars to reflect on how the cultural and social implications of this global event solicit a reorganization and reframing of some of the existing conceptual and theoretical tools that have shaped media and cultural studies as a field. Contributors moved from one specific keyword to consider how these notions are imbricated by the crisis, either COVID-19 specifically or in more general terms. This editorial provides an overview to the themed issue and highlights the benefit of considering the impact of the pandemic from a broader and longer perspective, which moves away from the language and rhetoric of emergency.

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Crosscurrents: Welfare

Cited by 7 publications

References 23 publications

The Artificial Recruiter: Risks of Discrimination in Employers’ Use of AI and Automated Decision‐Making

The Artificial Recruiter: Risks of Discrimination in Employers’ Use of AI and Automated Decision‐Making

Digital resignation and the datafied welfare state

Rethinking keywords in media and cultural studies during and beyond COVID-19: Editorial

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