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“Guilds” as Worker Empowerment and Control in a Chinese Data Work Platform
Get PDFData 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
Unlocking Augmented Reality Learning Design Based on Evidence From Empirical Cognitive Load Studies—A Systematic Literature Review
Get PDFBackground Despite the numerous positive effects of augmented reality (AR) on learning, previous research has shown ambiguous results regarding the cognitive demand on the learner arising from, for example, the overlay of virtual elements or novel interaction techniques. At the same time, the number of evidence‐based guidelines on designing AR is limited or focuses on global effects, primarily relying on media comparison studies, whose validity is criticised. Objective To guide the meaningful design of learning and training settings, this paper systematically reviews empirical research on AR design and synthesises the findings to develop evidence‐based recommendations for designing AR systems considering cognitive load. Methods We conducted a systematic literature review, initially screening 810 distinct papers and ultimately analysing findings from 27 publications, which report on 29 distinct experimental studies. This selection was based on rigorously defined inclusion and exclusion criteria, adhering to the PRISMA guidelines. Results and Conclusion The central value of this paper is the aggregation of existing evidence from empirical studies, resulting in 15 recommendations for AR design based on six design dimensions: Spatiality‐related, Interaction‐related, Contextuality‐related, Content‐related, Guidance‐related and Display Selection. Additionally, with three points for future research, this systematic literature review, first, stresses the need for more empirical evidence and value‐added studies. Second, learner characteristics that might influence cognitive load in AR‐based learning should be examined. Third, it advocates for the inclusion of measurements beyond the NASA‐TLX, and including more physiological measurements (e.g., eye‐tracking, EEG) to enhance the applicability of the results for learning and training situations
Too Far Away from the Job Market – Says Who? Linguistically Analyzing Rationales for AI-based Decisions Concerning Employment Support
Get PDFThis paper describes an AI-based decision-support system deployed by the Swedish Public Employment Service to assist decisions concerning jobseekers’ enrolment in an employment support initiative. Informed by previous research concerning explanations in relation to trust, appealability, and procedural fairness, as well as jobseekers’ needs and interests in relation to algorithmic decision-making, the study linguistically analyses the extent to which the system enables affected jobseekers to understand the basis of decisions and to appeal or take other actions in response to automated assessments. The study also analyses the degree to which rationales behind decisions accurately reflect the actual decision-making process. Several weaknesses in these regards are highlighted, largely resulting from the opacity of the statistical model and the linguistic choices behind the design of explanations. Potential strategies for increasing the explainability of the system as a means to meet the needs and interests of affected jobseekers are also discussed. More broadly, the study contributes to a better understanding of how the linguistic design of AI explanations can affect normative dimensions, such as trust and appealability.The Weizenbaum Institute is funded by the German Federal Ministry of Education and Research (BMBF
Weizenbaum Panel’s Literature Digest: November 2024
Get PDFDer Literatur Digest ist eine monatlich erscheinende Zusammenstellung des aktuellen Forschungsstandes zu Themen an der Schnittstelle zwischen Digitalisierung und Politik. Er präsentiert die neuesten Erkenntnisse zu Fragen der politischen Partizipation und guter Bürgerschaft in Zeiten der Digitalisierung.The Literature Digest is a monthly compilation of the current state of research on topics at the nexus of digitalization and politics. It presents the latest findings on issues of political participation and good citizenship in times of digitalization
Weizenbaum Panel’s Literature Digest: September 2024
Get PDFDer Literatur Digest ist eine monatlich erscheinende Zusammenstellung des aktuellen Forschungsstandes zu Themen an der Schnittstelle zwischen Digitalisierung und Politik. Er präsentiert die neuesten Erkenntnisse zu Fragen der politischen Partizipation und guter Bürgerschaft in Zeiten der Digitalisierung.The Literature Digest is a monthly compilation of the current state of research on topics at the nexus of digitalization and politics. It presents the latest findings on issues of political participation and good citizenship in times of digitalization
Challenges of and approaches to data collection across platforms and time: Conspiracy-related digital traces as examples of political contention
Get PDFTaking the example of conspiracy-related communication online as one form of contentious politics, this study examines the data collection challenges for multidimensional comparative research across platforms, time, and cultural embeddings. It compares the architectures and features relevant to data collection, access regimes, and use cultures for a set of digital platforms and communication venues. Differentiating between actor- and content-based strategies, this study discusses the potentials and limitations of these approaches, considering differences in platforms, temporal dynamics, and cultural embeddings as well as several layers of equivalence. The discussion highlights crucial insights into designing data collection strategies in multidimensional comparative studies.This study was supported by grants from the German Federal Ministry of Education and Research (grant numbers 13N16049 [in the context of the call for proposals Civil Security – Societies in Transition] and 16DII135 [in the context of the Weizenbaum Institute]). Dominik Schindler acknowledges support from the EPSRC (PhD studentship through the Department of Mathematics at Imperial College London) and from the Weizenbaum Institute (Research Fellowship)
Further Training in Industry 4.0 with AI Tutoring Systems - State of technology
Get PDFThe rapid development of Artificial Intelligence (AI) is constantly opening new
opportunities, particularly in training for the factory of the future. For employees,
this not only means a significant advantage in the actual manufacturing
process, but also in the field of continuing education. This paper provides an
overview of AI tutoring systems continuing education in the context of Industry
4.0 by presenting a categorization that discusses different approaches of AI
tutoring systems by learning methods, application areas and their respective
technologies. In addition, an outlook on the disruptive effect of generative AI
on AI tutoring systems in Industry 4.0 is given
Digital Sovereignty in times of AI: between perils of hegemonic agendas and possibilities of alternative approaches
Get PDFAlthough 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
An Introduction to Open Educational Resources and Their Implementation in Higher Education Worldwide
Get PDFThe digitization of (higher) education has exposed copyright infringement issues, as the unauthorized use of copyrighted materials has become more visible. This article explores the importance of open educational resources (OER) in higher education, focusing on their development, how they are understood, and the opportunities they offer. OER are defined as learning materials released under open licenses, allowing no-cost access, reuse, adaptation, and redistribution. The article discusses the OER movement, its milestones, and its integration into educational practice. It also presents arguments for OER: they enable free access to education, improve teaching practice, diminish legal issues, and foster open science. In addition, it highlights criticisms, including resistance from traditional publishers and concerns about marketing influence. The article concludes by examining current OER implementation in higher education and its promise of innovation. While OER are increasingly adopted, proprietary resources still dominate. The article emphasizes the need for educators to use open licenses meaningfully and innovatively and presents research on OER acceptance and usage. The monitoring of OER development in higher education is essential, but approaches may vary across countries.The Weizenbaum Institute is funded by the German Federal Ministry of Education and Research (BMBF
Silencing the Risk, Not the Whistle: A Semi-automated Text Sanitization Tool for Mitigating the Risk of Whistleblower Re-Identification
Get PDFWhistleblowing 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