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.
- 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’. […]
- 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
- 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.