Forschungsschwerpunkte

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https://www.weizenbaum-library.de/handle/id/304

Aktualisiertes Forschungsprogramm

Zum Auftakt der ersten Etablierungsphase im September 2022 hat das Weizenbaum-Institut sein Forschungsprogramm aktualisiert. Um die relevanten Herausforderungen und Chancen des digitalen Wandels für Individuen und Gesellschaften interdisziplinär, grundlagenorientiert und wertebasiert zu untersuchen, wird die Forschung fortan in vier interdisziplinären Forschungsschwerpunkten organisiert:

Digitale Technologien in der Gesellschaft: zwischen Teilhabechancen und neuen Ungleichheiten
Digitale Märkte und Öffentlichkeiten auf Plattformen: zwischen Gemeinwohl und wirtschaftlichen Imperativen
Organisation von Wissen: zwischen Offenheit und Exklusivität
Digitale Infrastrukturen in der Demokratie: zwischen Sicherheit und Freiheit

Jeder Schwerpunkt analysiert ein gesellschaftliches Spannungsverhältnis, das den gemeinsamen Bezug für die Arbeit in den jeweils vier Forschungsgruppen bildet, die einem Schwerpunkt zugeordnet sind. Flankiert und unterstützt werden die Forschungsgruppen vom neuen Weizenbaum Digital Science Center, das Forschungs-, Vernetzungs-, Orientierungs- und Infrastrukturleistungen für die interdisziplinäre Digitalisierungsforschung erbringt und die Kohärenz der Forschung stärkt.

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Gerade angezeigt 1 - 20 von 41

Articulation Work and Tinkering for Fairness in Machine Learning
(2024) Fahimi, Miriam; Russo, Mayra; Scott, Kristen M.; Vidal, Maria-Esther; Berendt, Bettina; Kinder-Kurlanda, Katharina
The field of fair AI aims to counter biased algorithms through computational modelling. However, it faces increasing criticism for perpetuating the use of overly technical and reductionist methods. As a result, novel approaches appear in the field to address more socially-oriented and interdisciplinary (SOI) perspectives on fair AI. In this paper, we take this dynamic as the starting point to study the tension between computer science (CS) and SOI research. By drawing on STS and CSCW theory, we position fair AI research as a matter of 'organizational alignment': what makes research 'doable' is the successful alignment of three levels of work organization (the social world, the laboratory, and the experiment). Based on qualitative interviews with CS researchers, we analyze the tasks, resources, and actors required for doable research in the case of fair AI. We find that CS researchers engage with SOI research to some extent, but organizational conditions, articulation work, and ambiguities of the social world constrain the doability of SOI research for them. Based on our findings, we identify and discuss problems for aligning CS and SOI as fair AI continues to evolve.
Away from Keyboard. Design research in the context of social and political participation
(transcript, 2022) Herlo, Bianca; Schütze, Konstanze; Hofhues, Sandra
Can civic data be counterdata and open data? Exploring the limits of data, contestation and governance
(Nomos, 2025) Shibuya, Yuya; Olojo, Seyi; Hamm, Andrea; Krishnan, Radhika; Pargman, Teresa Cerratto; Kox, Thomas; Ullrich, André; Zech, Herbert
The increasing surveillance by big tech companies or/and governments has raised concerns about the democratic and participatory structure of the datafied society. Meanwhile, over the course of the past decade, various bottom-up civic tech and digital civic initiatives have emerged to tackle pressing local issues, such as air pollution and disaster response, often via technology-mediated data collection, curation, analysis, design and visualisations, thus promoting democratic participation. In this article, we discuss how these data are understood in diverse contexts beyond the realm of civic tech and digital civics. In doing so, we explore the potential and limits of civic data by exploring the intersections of and differences between civic data and adjacent data-related concepts often used by civic tech communities themselves: counterdata and open data. Through our discursive exploration of these three data concepts, we conclude that understanding is limited when it comes to determining which data are ‘civic’, and that discussion of questions related to power structures, diversity and inclusion and infrastructuring of civic data has been minimal.
Design thinking capabilities in the digital world: A bibliometric analysis of emerging trends
(2023) Dragičević, Nikolina; Vladova, Gergana; Ullrich, André
Recent research suggests that design thinking practices may foster the development of needed capabilities in new digitalised landscapes. However, existing publications represent individual contributions, and we lack a holistic understanding of the value of design thinking in a digital world. No review, to date, has offered a holistic retrospection of this research. In response, in this bibliometric review, we aim to shed light on the intellectual structure of multidisciplinary design thinking literature related to capabilities relevant to the digital world in higher education and business settings, highlight current trends and suggest further studies to advance theoretical and empirical underpinnings. Our study addresses this aim using bibliometric methods—bibliographic coupling and co-word analysis as they are particularly suitable for identifying current trends and future research priorities at the forefront of the research. Overall, bibliometric analyses of the publications dealing with the related topics published in the last 10 years (extracted from the Web of Science database) expose six trends and two possible future research developments highlighting the expanding scope of the design thinking scientific field related to capabilities required for the (more sustainable and human-centric) digital world. Relatedly, design thinking becomes a relevant approach to be included in higher education curricula and human resources training to prepare students and workers for the changing work demands. This paper is well-suited for education and business practitioners seeking to embed design thinking capabilities in their curricula and for design thinking and other scholars wanting to understand the field and possible directions for future research.
Designforschung im Kontext sozialer und politischer Partizipation
(transcript Verlag, 2022) Herlo, Bianca; Hofhues, Sandra; Schütze, Konstanze
Digital Sovereignty in times of AI: between perils of hegemonic agendas and possibilities of alternative approaches
(2024) Costa Barbosa, Alexandre; Herlo, Bianca; Joost, Gesche
Although it has been on the agenda for over a decade, the importance of digital sovereignty has recently increased. Nations-states worldwide have developed policies or expressed through speeches the need to safeguard their interests in the digital realm. The current technological frontier is artificial intelligence (AI). Hence, digital sovereignty agendas now encompass the complexities introduced by AI. This article explores contemporary discourses on digital sovereignty, highlighting how different ideological positions shape these conversations. Current discussions reveal a multifaceted field where sovereignty is interpreted through varied lenses, directly influencing the governance of technologies such as AI. Predominant perspectives often focus on state, market, or individual sovereignty over data, algorithms, and AI models. However, through document and discourse analysis, the article examines alternative approaches such as sustainable, grassroots, and feminist digital sovereignties and those led by communities or indigenous peoples. These visions challenge the mainstream by emphasizing autonomy, inclusion, and sustainability in managing critical AI resources, including computing, databases, data, and algorithm governance. By analyzing these approaches, the article identifies principles that can foster more diverse, democratic, and virtuous AI development. Finds points out that participatory governance and the development of emancipatory technologies are essential to navigating the ethical and practical issues that emerge at the intersection of digital sovereignty and AI. In a normative way, the article concludes by reflecting on how these alternative discourses can influence the future of AI, pointing to paths that could lead to a more inclusive and sovereign AI development aligned with collective and environmental values. Future research could explore how these sovereignty conceptions catalyze an AI's evolution to align with collective digital self-determination and more conscious and equitable resource management practices.
Diversity and bias in DBpedia and Wikidata as a challenge for text-analysis tools
(2023) Berendt, Bettina; Karadeniz, Oğuz Özgür; Kıyak, Sercan; Mertens, Stefan; d’Haenens, Leen
Diversity Searcher is a tool originally developed to help analyse diversity in news media texts. It relies on automated content analysis and thus rests on prior assumptions and depends on certain design choices related to diversity. One such design choice is the external knowledge source(s) used. In this article, we discuss implications that these sources can have on the results of content analysis. We compare two data sources that Diversity Searcher has worked with – DBpedia and Wikidata – with respect to their ontological coverage and diversity, and describe implications for the resulting analyses of text corpora. We describe a case study of the relative over- or underrepresentation of Belgian political parties between 1990 and 2020. In particular, we found a staggering overrepresentation of the political right in the English-language DBpedia.
Documenting Data Production Processes: A Participatory Approach for Data Work
(2022) Miceli, Milagros; Yang, Tianling; Alvarado Garcia, Adriana; Posada, Julian; Wang, Sonja Mei; Pohl,Marc; Hanna, Alex
The opacity of machine learning data is a significant threat to ethical data work and intelligible systems. Previous research has addressed this issue by proposing standardized checklists to document datasets. This paper expands that field of inquiry by proposing a shift of perspective: from documenting datasets towards documenting data production. We draw on participatory design and collaborate with data workers at two companies located in Bulgaria and Argentina, where the collection and annotation of data for machine learning are outsourced. Our investigation comprises 2.5 years of research, including 33 semi-structured interviews, five co-design workshops, the development of prototypes, and several feedback instances with participants. We identify key challenges and requirements related to the integration of documentation practices in real-world data production scenarios. Our findings comprise important design considerations and highlight the value of designing data documentation based on the needs of data workers. We argue that a view of documentation as a boundary object, i.e., an object that can be used differently across organizations and teams but holds enough immutable content to maintain integrity, can be useful when designing documentation to retrieve heterogeneous, often distributed, contexts of data production.
Dreaming of AI: environmental sustainability and the promise of participation
(2024) Zehner, Nicolas; Ullrich, André
There is widespread consensus among policymakers that climate change and digitalisation constitute the most pressing global transformations shaping human life in the 21st century. Seeking to address the challenges arising at this juncture, governments, technologists and scientists alike increasingly herald artificial intelligence (AI) as a vehicle to propel climate change mitigation and adaptation. In this paper, we explore the intersection of digitalisation and climate change by examining the deployment of AI in government-led climate action. Building on participant observations conducted in the context of the “Civic Tech Lab for Green”—a government-funded public interest AI initiative—and eight expert interviews, we investigate how AI shapes the negotiation of environmental sustainability as an issue of public interest. Challenging the prescribed means–end relationship between AI and environmental protection, we argue that the unquestioned investment in AI curtails political imagination and displaces discussion of climate “problems” and possible “solutions” with “technology education”. This line of argumentation is rooted in empirical findings that illuminate three key tensions in current coproduction efforts: “AI talk vs. AI walk”, “civics washing vs. civics involvement” and “public invitation vs. public participation”. Emphasising the importance of re-exploring the innovative state in climate governance, this paper extends academic literature in science and technology studies that examines
E-participation in municipalities services: A systematic literature review of evaluation approaches
(2025) Ietto, Beatrice; Ullrich, André
Evaluating e-participation initiatives is critical to understanding their impact on democratic processes, public policy, and societal welfare. However, existing evaluation frameworks often neglect the complexities of multi-stakeholder environments. Through a systematic literature review of 47 empirical cases, this gap is addressed by developing a typology of evaluation categories (supply-side, user activity, public value, political, and societal impact) and a comprehensive evaluation framework that accounts for the diverse objectives of all the involved stakeholders — technology providers, local governments, and citizens. By integrating short-term engagement metrics and long-term societal outcomes, the framework ensures more accurate assessments of e-participation success. We argue that adopting a multi- stakeholder approach in evaluation can significantly enhance the effectiveness, inclusivity, and sustainability of e-participation initiatives. Our findings challenge current evaluation practices and provide guidance for practitioners aiming to optimize governance, improve public services, and empower citizens through more robust evaluation methods. This study lays the foundation for systematic evaluation methods that consider stakeholder objectives, crucial for advancing e- participation research, and policy.
Employee involvement and participation in digital transformation: a combined analysis of literature and practitioners' expertise
(2023) Ullrich, André; Reißig, Malte; Niehoff, Silke; Beier, Grischa
This paper provides a systematization of the existing body of literature on both employee participation goals and the intervention formats in the context of organizational change. Furthermore, degrees of employee involvement that the intervention formats address are identified and related to the goals of employee participation. On this basis, determinants of employee involvement and participation in the context of digital transformation are unveiled.
Empowering smart regions: addressing challenges and leveraging enabling factors in municipal digital transformation
(Nomos, 2025) Brandenburger, Bonny; Hamm, Andrea; Krohn, Caroline; Sühlmann-Faul, Felix; Atug, Manuel; Döpp, Nicole; Ullrich, André; Kox, Thomas; Ullrich, André; Zech, Herbert
The advancing digital transformation of society creates a wide range of opportunities for improved access to information and resources that contribute to ensuring the availability of public services and the development of sustainable living spaces. This underlying potential does not only apply to urban areas; digitalisation projects are also being implemented in rural municipalities in order to exploit the potential of digital transformation. Nevertheless, the field of so-called smart regions has yet to receive substantial focus in research. To understand the specific challenges and enabling factors of digital transformation activities in urban-rural areas, a workshop was organised with the cooperation of municipal representatives of a model region in Schleswig-Holstein, Germany. Therein, specific technical, economic and social challenges as well as enabling factors of municipal digitalisation projects aimed at developing smart regions were identified. The results show that the success of digital transformation meas‐ ures in urban-rural areas is not only determined by the expansion of a corresponding technical infrastructure but in particular by the acceptance of citizens and municipality employees, as well as economic viability. This research further informs municipalities and future researchers on the critical factors required to effectively conduct digitalisation projects in the smart region context.
Gestaltungspraktiken in transdisziplinärer Forschung
(transcript, 2024) Herlo, Bianca; Ebert, Iris; Rahn, Sebastian; Rodatz, Christoph
Gewissensbisse - Fallbeispiele zu ethischen Problemen der Informatik
(transcript Verlag, 2023) Class, Christina B.; Coy, Wolfgang; Kurz, Constanze; Obert, Otto; Rehak, Rainer; Trinitis, Carsten; Ullrich, Stefan; Weber-Wulff, Debora
Die vielfältigen Möglichkeiten moderner IT-Systeme bringen drängende ethische Probleme mit sich. Neben der offensichtlichen Frage nach einer moralisch tragbaren Verwendung von Informationstechnologien sind ebenso die Aspekte des Entwerfens, Herstellens und Betreibens derselben entscheidend. Die Beiträge setzen sich mit dem Konfliktpotenzial zwischen Technik und Ethik auseinander, indem sie lebensnahe Fallbeispiele vorstellen und fragenbasiert zur Diskussion einladen. Damit liefern sie eine praktische Herangehensweise zum gemeinsamen Nachdenken über moralische Gebote und ethischen Umgang mit IT-Systemen und ihren Möglichkeiten. Der Band eignet sich damit in hervorragender Weise zum Vermitteln und Erlernen von ethischer Reflexions- und Handlungskompetenz in der Informatik sowie im Umgang mit IT-Technologien überhaupt.
“Guilds” as Worker Empowerment and Control in a Chinese Data Work Platform
(Association for Computing Machinery, 2024) Yang, Tianling; Miceli, Milagros
Data work plays a fundamental role in the development of algorithmic systems and the AI industry. It is often performed in business process outsourcing (BPO) companies and crowdsourcing platforms, involving a global and distributed workforce as well as networks of collaborative actors. Previous work on community building among data workers centers organization and mutual support or focuses on the structuring and instrumentalization of crowdworker groups for complicated projects. We add to these lines of research by focusing on a specific form of community building encouraged and facilitated by platforms in China: guilds. Based on ethnographic work on a Chinese crowdsourcing platform and 14 semi-structured interviews with data workers, our findings show that guilds are a form of both worker empowerment and control. With this work, we add a nuanced empirical case to the interconnection of BPOs, online communities and crowdsourcing platforms in the current data production sector in China, thus expanding previous investigations on global perspectives of data production. We discuss guilds in relation to individual workers and highlight their effects on data work, including efficient coordination, enhanced standardization, and flattened power structure.
“Guilds” as Worker Empowerment and Control in a Chinese Data Work Platform
(Association for Computing Machinery, 2024) Yang, Tianling; Miceli, Milagros
Data work plays a fundamental role in the development of algorithmic systems and the AI industry. It is often performed in business process outsourcing (BPO) companies and crowdsourcing platforms, involving a global and distributed workforce as well as networks of collaborative actors. Previous work on community building among data workers centers organization and mutual support or focuses on the structuring and instrumentalization of crowdworker groups for complicated projects. We add to these lines of research by focusing on a specific form of community building encouraged and facilitated by platforms in China: guilds. Based on ethnographic work on a Chinese crowdsourcing platform and 14 semi-structured interviews with data workers, our findings show that guilds are a form of both worker empowerment and control. With this work, we add a nuanced empirical case to the interconnection of BPOs, online communities and crowdsourcing platforms in the current data production sector in China, thus expanding previous investigations on global perspectives of data production. We discuss guilds in relation to individual workers and highlight their effects on data work, including efficient coordination, enhanced standardization, and flattened power structure.
How Far Can It Go? On Intrinsic Gender Bias Mitigation for Text Classification
(Association for Computational Linguistics, 2023) Tokpo, Ewoenam Kwaku; Delobelle, Pieter; Berendt, Bettina; Calders, Toon
To mitigate gender bias in contextualized language models, different intrinsic mitigation strategies have been proposed, alongside many bias metrics. Considering that the end use of these language models is for downstream tasks like text classification, it is important to understand how these intrinsic bias mitigation strategies actually translate to fairness in downstream tasks and the extent of this. In this work, we design a probe to investigate the effects that some of the major intrinsic gender bias mitigation strategies have on downstream text classification tasks. We discover that instead of resolving gender bias, intrinsic mitigation techniques and metrics are able to hide it in such a way that significant gender information is retained in the embeddings. Furthermore, we show that each mitigation technique is able to hide the bias from some of the intrinsic bias measures but not all, and each intrinsic bias measure can be fooled by some mitigation techniques, but not all. We confirm experimentally, that none of the intrinsic mitigation techniques used without any other fairness intervention is able to consistently impact extrinsic bias. We recommend that intrinsic bias mitigation techniques should be combined with other fairness interventions for downstream tasks.
Investigating Innovation Diffusion in Gender-Specific Medicine: Insights from Social Network Analysis
(2024) Baum, Katharina; Baumann, Annika; Batzel, Katharina
The field of healthcare is characterized by constant innovation, with gender-specific medicine emerging as a new subfield that addresses sex and gender disparities in clinical manifestations, outcomes, treatment, and prevention of disease. Despite its importance, the adoption of gender-specific medicine remains understudied, posing potential risks to patient outcomes due to a lack of awareness of the topic. Building on the Innovation Decision Process Theory, this study examines the spread of information about gender-specific medicine in online networks. The study applies social network analysis to a Twitter dataset reflecting online discussions about the topic to gain insights into its adoption by health professionals and patients online. Results show that the network has a community structure with limited information exchange between sub-communities and that mainly medical experts dominate the discussion. The findings suggest that the adoption of gender-specific medicine might be in its early stages, focused on knowledge exchange. Understanding the diffusion of gender-specific medicine among medical professionals and patients may facilitate its adoption and ultimately improve health outcomes.
Künstliche Intelligenz zum Schutz der natürlichen Welt? Einführung in ethische und gesellschaftsfreundliche Künstliche Intelligenz (KI) mit Blick auf den Naturschutz
(Bundesamt für Naturschutz, 2023) Rehak, Rainer; Mrogenda, Klemens; Davis, Marlen; Feit, Ute; Schneider, Christian
Lost in moderation: How commercial content moderation apis over- and under-moderate group-targeted hate speech and linguistic variations
(Association for Computing Machinery, 2025) Hartmann, David; Oueslati, Amin; Staufer, Dimitri; Pohlmann, Lena; Munzert, Simon; Heuer, Hendrik
Commercial content moderation APIs are marketed as scalable solutions to combat online hate speech. However, the reliance on these APIs risks both silencing legitimate speech, called over-moderation, and failing to protect online platforms from harmful speech, known as under-moderation. To assess such risks, this paper introduces a framework for auditing black-box NLP systems. Using the framework, we systematically evaluate five widely used commercial content moderation APIs. Analyzing five million queries based on four datasets, we find that APIs frequently rely on group identity terms, such as “black”, to predict hate speech. While OpenAI’s and Amazon’s services perform slightly better, all providers under-moderate implicit hate speech, which uses codified messages, especially against LGBTQIA+ individuals. Simultaneously, they over-moderate counter-speech, reclaimed slurs and content related to Black, LGBTQIA+, Jewish, and Muslim people. We recommend that API providers offer better guidance on API implementation and threshold setting and more transparency on their APIs’ limitations.Warning: This paper contains offensive and hateful terms and concepts. We have chosen to reproduce these terms for reasons of transparency.

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Auflistung Forschungsschwerpunkte nach Forschungsbereichen "Digitale Technologien in der Gesellschaft"

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