Digitale Technologien in der Gesellschaft

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In diesem Forschungsschwerpunkt sollen der Zusammenhang zwischen Digitalisierung, Teilhabe und Ungleichheit erforscht, die Nutzung digitaler Technologien für Teilhabechancen gestaltend erprobt und gegen neue Ungleichheiten interveniert werden. Dafür werden Perspektiven der Wirtschaftsinformatik, der Designforschung und der Informatik zusammengeführt.

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    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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    Uncertain Journeys into Digital Futures: Inter- and Transdisciplinary Research for Mitigating Wicked Societal and Environmental Problems
    (Nomos, 2025) Kox, Thomas; Ullrich, André; Zech, Herbert
    The Weizenbaum Institute organised its sixth Annual Conference on the topic of “Uncertain journeys into digital futures” in Berlin in June 2024. The conference focused on the challenge of the digital transformation and the socio-ecological transformation of society which are closely interlinked and crucial for prospering futures of humanity. Challenges include the protection of people, democratic institutions and the environment, as well as enabling participation in shaping changes and an inclusive and fair life. Relevant topics for addressing these challenges are smart cities and urban transformation, digital technologies for sustainability, social justice, governance and citizen participation as well as ideas and visions of the future.
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    Shaping uncertain journeys into digital futures - perspectives on the digital and socio-ecological transformation
    (Nomos, 2025) Ullrich, André; Kox, Thomas; Zech, Herbert; Kox, Thomas; Ullrich, André; Zech, Herbert
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    Silencing the Risk, Not the Whistle: A Semi-automated Text Sanitization Tool for Mitigating the Risk of Whistleblower Re-Identification
    (ACM, 2024) Staufer, Dimitri; Pallas, Frank; Berendt, Bettina
    Whistleblowing is essential for ensuring transparency and accountability in both public and private sectors. However, (potential) whistleblowers often fear or face retaliation, even when report- ing anonymously. The specific content of their disclosures and their distinct writing style may re-identify them as the source. Legal measures, such as the EU Whistleblower Directive, are limited in their scope and effectiveness. Therefore, computational methods to prevent re-identification are important complementary tools for encouraging whistleblowers to come forward. However, current text sanitization tools follow a one-size-fits-all approach and take an overly limited view of anonymity. They aim to mitigate identification risk by replacing typical high-risk words (such as person names and other labels of named entities) and combinations thereof with placeholders. Such an approach, however, is inadequate for the whistleblowing scenario since it neglects further re-identification potential in textual features, including the whistleblower’s writing style. Therefore, we propose, implement, and evaluate a novel classification and mitigation strategy for rewriting texts that involves the whistleblower in the assessment of the risk and utility. Our prototypical tool semi-automatically evaluates risk at the word/term level and applies risk-adapted anonymization techniques to produce a grammatically disjointed yet appropriately sanitized text. We then use a Large Language Model (LLM) that we fine-tuned for paraphrasing to render this text coherent and style-neutral. We evaluate our tool’s effectiveness using court cases from the European Court of Human Rights (ECHR) and excerpts from a real-world whistleblower testimony and measure the protection against authorship attribution attacks and utility loss statistically using the popular IMDb62 movie reviews dataset, which consists of 62 individuals. Our method can significantly reduce authorship attribution accuracy from 98.81% to 31.22%, while preserving up to 73.1% of the original content’s semantics, as measured by the established cosine similarity of sentence embeddings.
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    “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.