Proceedings of the Weizenbaum Conference 2023. AI, Big Data, Social Media and People on the Move
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- ItemWhy Does the AI Say That I Am Too Far Away from the Job Market?(Weizenbaum Institute, 2023) Berman, AlexanderAs artificial intelligence (AI) is increasingly being deployed in various domains such as healthcare (Qayyum et al., 2021), finance (Dastile, Celik & Potsane, 2020) and public welfare (Saxena et al., 2020; Carney, 2020), there is a growing need for understanding how stakeholders are affected by AI (Vaassen, 2022) and how to design and present explanations of AI-based decisions in ways that humans can understand and use (Miller, 2019). This paper contributes to these efforts by examining an AI-based decision-support system (DSS) launched by the Swedish Public Employment Service (PES) in 2020. Specifically, the study investigates to what extent the studied system enables affected jobseekers to understand the basis of AI-assisted decisions, to negotiate or contest dispreferred decisions, and to use the AI as a tool for increasing their job chances.
- ItemProceedings of the Weizenbaum Conference 2023. AI, Big Data, Social Media and People on the Move(Weizenbaum Institute, 2023) Berendt, Bettina; Krzywdzinski, Martin; Kuznetsova, ElizavetaThe 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.
- ItemThe Problems of the Automation Bias in the Public Sector: A Legal Perspective(Weizenbaum Institute, 2023) Ruschemeier, HannahThe 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.
- ItemDigital Accountability: The Untapped Potential of Participation when Using Technology in Humanitarian Action(Weizenbaum Institute, 2023) Düchting, AndreaOver the past decades, digital technologies have seen a massive increase in use and have profoundly shaped the humanitarian sector. Their exponential growth has greatly increased the amount of data to be managed and accelerated the speed with which information travels (ALNAP 2022; OCHA 2021). This growth triggered discussions around the efficiency of necessary humanitarian services to respond to rising needs and sector-wide funding cuts. The request for more evidence-based programming, improved coordination, and increased accountability pushed many humanitarian organisations to ‘go digital’. […]
- ItemStandardization and Heterogenization: The Automation of Management and the Multiplication of Labour(Weizenbaum Institute, 2023) Altenried, MoritzAlgorithmic 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.