Proceedings of the Weizenbaum Conference 2023. AI, Big Data, Social Media and People on the Move

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    Algorithmic Management in the Food Delivery Sector – A Contested Terrain? Evidence from a Form-Level Case-Study on Algorithmic Management and Co-Determination
    (Weizenbaum Institute, 2023) Wotschack, Philip; Hellbach, Leon; Butollo, Florian; Ziour, Jordi
    Forms of algorithmic management (AM) play an essential role in organizing food delivery work by deploying AI-based systems for coordinating driver routes. Given the risks of precarity and threats posed by AM that are typically related to (migrant) platform work, the question arises to what extent structures of co-determination are able to positively shape this type of work and the technologies involved. Based on an intense case-study in a large food delivery company, this paper is guided by three questions: (1) How is algorithm-based management and control used by the company? (2) How is it perceived by the couriers, also in relation to other aspects of their work? (3) What are the works council’s priorities, strategies, and achievements regarding co-determination practices? Contrary to the prevalent perception in the literature on the subject of AM, our analysis shows that human agency is still pivotal when algorithm-based systems are used to manage work processes. While data- and AM-related issues do not represent a central area of conflict, we find that co-determination rights in this domain can translate into a powerful bargaining resource of the works council with regard to the companies’ digital business model. Our study also shows that algorithmic management poses problems of non-transparency and information asymmetry, which calls for new forms and procedures of co-determination.
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    The Problems of the Automation Bias in the Public Sector: A Legal Perspective
    (Weizenbaum Institute, 2023) Ruschemeier, Hannah
    The automation bias describes the phenomenon, proven in behavioural psychology, that people place excessive trust in the decision suggestions of machines. The law currently sees a dichotomy—and covers only fully automated decisions, and not those involving human decision makers at any stage of the process. However, the widespread use of such systems, for example to inform decisions in education or benefits administration, creates a leverage effect and increases the number of people affected. Particularly in environments where people routinely have to make a large number of similar decisions, the risk of automation bias increases. As an example, automated decisions providing suggestions for job placements illustrate the particular challenges of decision support systems in the public sector. So far, the risks have not been sufficiently addressed in egislation, as the analysis of the GDPR and the draft Artificial Intelligence Act show. I argue for the need for regulation and present initial approaches.
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    Proceedings of the Weizenbaum Conference 2023. AI, Big Data, Social Media and People on the Move
    (Weizenbaum Institute, 2023) Berendt, Bettina; Krzywdzinski, Martin; Kuznetsova, Elizaveta
    The contributions focus on the question of what role different digital technologies play for “people on the move” - with “people on the move” being understood both spatially (migration and flight) and in terms of economic and social change (changing working conditions, access conditions). The authors discuss phenomena such as disinformation and algorithmic bias from different perspectives, and the possibilities, limits and dangers of generative artificial intelligence.
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    AI and Inequality in Hiring and Recruiting: A Field Scan
    (Weizenbaum Institute, 2023) Dinika, Adio-Adet; Sloane, Mona
    This paper provides a field scan of scholarly work on AI and hiring. It addresses the issue that there still is no comprehensive understanding of how technical, social science, and managerial scholarships around AI bias, recruiting, and inequality in the labor market intersect, particularly vis-à-vis the STEM field. It reports on a semi-systematic literature review and identifies three overlapping meta themes: productivity, gender, and AI bias. It critically discusses these themes and makes recommendations for future work
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    Standardization and Heterogenization: The Automation of Management and the Multiplication of Labour
    (Weizenbaum Institute, 2023) Altenried, Moritz
    Algorithmic management is increasingly used to (semi-)automatically organise, measure and control labour in many sectors and industries. Based on empirical research in the (online and location-bound) gig economy, the paper argues that this digital automation of management allows for the quick and flexible inclusion of a broad range of workers in very diverse situations into production. This is shown, firstly, by the example of crowdwork platforms and their ability to integrate diverse and spatially distributed workers into labour processes. Secondly, the paper analyses the role of migrant labour for the urban gig economy and argues, that here, too, digital technologies and algorithmic management are to be understood as being part and parcel of a multifaceted process of the heterogenization of workforces. This particular effect and quality of algorithmic management and digital standardization is conceptually analysed in the framework of a multiplication of labour.