Proceedings of the Weizenbaum Conference 2023. AI, Big Data, Social Media and People on the Move
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- ItemAI and Inequality in Hiring and Recruiting: A Field Scan(Weizenbaum Institute, 2023) Dinika, Adio-Adet; Sloane, MonaThis 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
- ItemAlgorithmic 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, JordiForms 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.
- 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’. […]
- ItemExploring the German-Language Twittersphere: Network Analysis of Discussions on the Syrian and Ukrainian Refugee Crises(Weizenbaum Institute, 2023) Kiyak, Sercan; De Coninck, David; Mertens, Stefan; d’Haenens, LeenThis study conducts a comparative analysis of Twitter communication networks relating to the Syrian and Ukrainian refugee crises. Employing a network analysis approach, the study uses approximately 660,000 tweets to gain insights into the online discussion communities surrounding these crises. Tweets specifically discussing Syrian refugees were collected between 2015 and 2023, while those about Ukrainians were harvested from 2022 to 2023, utilizing the full-archive search endpoint of the Twitter API. By transforming retweets into communication networks between users, the study investigates the community structure within these networks. The findings reveal that the online anti-refugee community is smaller in size, more active, highly interconnected, and transcends national boundaries, in contrast to the opposing communities. These results underscore the need for increased social media engagement of pro-refugee voices and improved moderation practices to foster a more inclusive virtual public sphere.
- 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.
- 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.
- 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.
- 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.