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Search Results
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The Problem of Sustainable AI: A Critical Assessment of an Emerging Phenomenon
Recently, the notion of “sustainable Artificial Intelligence (AI)” has gained traction. The contention is that AI technologies hold promise for addressing climate challenges by providing sustainable solutions. In that way, sustainable AI is supposed to harness AI’s capabilities while upholding ethical standards and minimizing its resources use, such as its carbon footprint. In answer to this recent trend, this paper critically questions the very conception of sustainable AI. Drawing on philosophy of technology and critical materialist thinking, it aims to uncover the dominant interests and hegemonic narratives driving sustainable AI developments. The paper begins by outlining the concept of sustainable AI. It then explores the hegemonic power structures and socio-economic dynamics behind AI technologies. Concretely, I show how the promises of sustainable AI largely rely on narratives of efficiency and progress, and work by invoking myths and images of a super-intelligence saving humanity. Following this, I highlight that sustainable AI is the technical solution to the climate crisis from a techno-solutionist vantage point simply reproducing the status quo. The enthusiasm for sustainable AI primarily serves hegemonic interests, rather than genuinely aiming for resource-friendly and ethical solutions. The paper concludes with the observation that if we want true climate action, sustainable AI is not the way to go.
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Beyond Open Access: Open Educational Resources for Legal Clarity, Sustainability, and Digital Sovereignty in European University Alliances
Open educational resources (OER) are widely recognized for improving access to education and enabling the sharing of knowledge. However, in the context of European university alliances such as Unite!, OER offer additional, often underappreciated benefits that are crucial for cross-border collaboration and sustainable development in higher education. This paper explores three key aspects of OER that are particularly relevant to European alliances. First, OER enable the legally secure use of educational resources across national borders, addressing uncertainties about copyright laws, particularly for translations and adaptations. This ensures compliance with different legal frameworks while fostering collaboration. Second, OER support sustainability by ensuring that investments in educational materials are not limited by restrictive usage rights. This is especially critical in alliances where shared resources are central to fostering long-term cooperation and aligning with sustainability goals, a priority for Unite!. Finally, OER contribute to digital sovereignty by empowering institutions and educators to create, adapt, and share resources without relying on proprietary platforms or licenses. This coincides with European alliances’ broader strategic objective of promoting autonomy and resilience in their digital ecosystems. By highlighting these often-overlooked benefits of OER, the present research aims to broaden the perspective on their strategic importance in fostering collaboration, sustainability, and sovereignty within European university alliances.
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Can Sustainable Shopping Recommendations in Online Retail Help Reduce Global Warming? Assessing the Direct and Indirect Climate Impact of Modern Software
Two dominant and contradictory narratives describe the apparent contribution of information and communication technology (ICT) to climate change. On the one hand, ICT can reduce global greenhouse gas (GHG) emissions by, for example, supporting energy efficiency or promoting sustainable consumption. On the other hand, the increased energy demands of emerging software components leveraging artificial intelligence or machine learning can be directly and indirectly responsible for GHG emissions. This makes it critical to assess whether ICT mitigates or exacerbates net climate impacts and the contributing factors. The impacts of software have received relatively little attention and require the development of new approaches to conduct such assessments. In particular, the net effect of complex real-world applications is frequently not measured. In this study, we provide a detailed step-by-step assessment to quantify the net global warming potential of an online shopping recommendation system that encourages users to make sustainable consumption decisions. We consider the energy consumed and associated GHG emissions in the development and use of the software and compare these to the potentially avoided GHG emissions associated with more sustainable recommended options. The results demonstrate that the software has the potential to indirectly avoid more emissions than it causes and that changes at different steps of the software can amplify this.
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Sustainable Artificial Intelligence: Critical and Constructive Reflections on Promises and Solutions, Amplifications and Contradictions
The developments in the field of artificial intelligence (AI) have seen many ups and downs since AI’s infancy. Recently, however, surprisingly powerful AI systems have been developed and are widely considered as silver bullets for any kind of social, ecological, political, scientific, or economic problem. However, the critical consideration of AI developments – especially their implications for society and the environment – has not been cultivated to the same extent. This imbalance leaves plenty of room for unreflective belief in technological progress and accompanying “techno-solutionism.” In order to inform and advance the debate regarding sustainability-oriented AI and the sustainability of AI itself, we compiled this thematic issue with reflections on the promises and solutions, amplifications and contradictions created by introducing AI into the sustainability endeavor and introducing sustainability-related application cases into AI development.
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AI Literacy for the Common Good
Artificial Intelligence (AI) does not provide solutions to pressing social questions, such as those pertaining to a peaceful, sustainable, and socially acceptable world. However, when employed in a purposeful and critically reflective manner, it can assist in formulating more effective inquiries that can enable a better understanding of the terms “AI” and “common good.” Through implementation in response to sustainability issues and given its potential as an inclusive technology, AI could be a powerful and useful tool for the common good. Despite the possibility of useful machine learning applications in terms of a positive cost-benefit calculation for its life cycle energy and resources, the majority of AI is far too energy-hungry for model training and to scale inferences. Despite the considerable variation observed in terms of certain aspects, it is evident that AI is currently neither sustainable in itself nor primarily used for sustainability purposes to address the grand challenges of global society in a world characterized by rapid acceleration. This demands a critical understanding of how AI systems work to enable society to decide upon the areas in which we should, can, or even definitely must not use AI. Based on the UNESCO Framework for AI Competency and the Dagstuhl Declaration of the German Informatics Society, we advocate for a type of critical AI literacy that can be best taught through practical use, that is, “learning by making.” This approach leads to a concise overview of existing options that facilitate a more reflective approach to using and understanding AI, including its potential and limitations. We conclude with a practical example.
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Potentials and Limitations of Active Learning: For the Reduction of Energy Consumption During Model Training
This article investigates the potential and limitations of using Active Learning (AL) to reduce AI’s carbon footprint and increase the accessibility of machine learning to low-resource projects. First, this paper reviews the recent literature on sustainable AI. The core of the article concerns AL as an emissions reduction technique. Because AL reduces the required data for model training, one can hypothesize that energy consumption and, accordingly, carbon emissions – also decreases. This paper tests this assumption. The leading questions concern whether AL is more efficient than traditional data sampling strategies and how we can optimize AL for sustainability. The experiments show that the benefit of AL strongly depends on its parameter settings and the data set size. Only in limited scenarios does the size reduction outweigh the computational costs for AL. For projects with more resources for annotations, AL is beneficial from an ecological perspective and should ideally be paired with model compression techniques. For smaller projects, however, AL can even have a negative impact on machine learning’s carbon footprint.
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Comparative Analysis of the Essential Factors for the Adoption of Massive Open Online Courses in Higher Education of a Developing Country: Pre and Post COVID-19
Although massive open online courses (MOOCs) offer numerous benefits to students, developing countries are still in the early stages of promoting their implementation. This study aims to investigate how the factors influencing MOOC adoption have evolved in response to the increased usage of online courses during the pandemic. The proposed model is based on the Technology Acceptance Model, and research hypotheses are presented based on six different factors: Perceived Usefulness, Perceived Ease of Use, Openness, Self-Efficacy, Quality of Service, and Reputation of the MOOC Provider. To test these hypotheses two surveys were conducted, one before and one after the COVID-19 period. Analyzing the data from these two time periods provides insight into the level of influence each of these factors has had on increased MOOC usage. Survey data was tested using the novel Partial Least Squares-Artificial Neural Network approach, which can effectively analyze complex human decisions. The findings indicate that Perceived Usefulness was the most influential factor in the adoption of MOOCs both before and after the COVID-19 pandemic. Interestingly, changes have been observed in the impact of Openness between the pre-pandemic and post-pandemic periods.
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Participatory, Agile, Co-creative: Identifying Topics for a Future-Oriented, Innovative Research on Digital Transformation
The importance of an adaptive and participatory scientific research process outside of the proverbial ivory tower is increasing. This is especially true in research on digital transformation, where topics are investigated in the context of their multidimensional socio-technological interdependencies. It is key to understand how research on digital transformation responds to these complexities, to what extent citizens’ needs are effectively integrated as areas of scientific exploration, and how up-to-date topics can be identified. In commercial industry endevours, for example, the participation and collaboration of different stakeholders are seen as fundamental parts of work processes in order to create and leverage inter- and transdisciplinary synergies. Scientific research also has a promising history of different participatory approaches. In this context, we suggest a concept for the adaptation and implementation of such approaches to enable participatory, agile, and co-creative academic research. Our example is a structured process based on the innovation framework “Double Diamond,” which is implemented to identify relevant topics for research on digital transformation. This process – characterized by a continuous alternation between collecting and condensing findings – included five qualitative and quantitative studies. The results of these studies are presented and discussed considering the specific needs and values of participatory approaches in research on digital transformation.
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Human Experience and AI Regulation: What European Union Law Brings to Digital Technology Ethics
Although nearly all artificial intelligence (AI) regulatory documents now reference the importance of human-centering digital systems, we frequently see AI ethics itself reduced to limited concerns, such as bias and, sometimes, power consumption. Although their impacts on human lives and our ecosystem render both of these absolutely critical, the ethical and regulatory challenges and obligations relating to AI do not stop there. Joseph Weizenbaum described the potential abuse of intelligent systems to make inhuman cruelty and acts of war more emotionally accessible to human operators. But more than this, he highlighted the need to solve the social issues that facilitate violent acts of war, and the immense potential the use of computers offers in this context. The present article reviews how the EU’s digital regulatory legislation—well enforced—could help us address such concerns. I begin by reviewing why the EU leads in this area, considering the legitimacy of its actions both regionally and globally. I then review the legislation already protecting us—the General Data Protection Regulation, the Digital Services Act, and the Digital Markets Act—and consider their role in achieving Weizenbaum’s goals. Finally, I consider the almost-promulgated AI Act before concluding with a brief discussion of the potential for future enforcement and more global regulatory cooperation.
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Commodification and Disruption: Theorizing Digital Capitalism
There is little disagreement that digital technologies are transforming contemporary economies and societies. However, scholars have only begun to systematically think about how digitalization – the process whereby more and more of what we say, think, and do becomes mediated by digital technologies – is both driven by and transformative of capitalism. This paper argues that when one speaks about digitalization, one cannot be silent about capitalism. It reconstructs commodification and disruption as key features of capitalist development. It then shows how three digital revolutions – the platform, (big) data, and artificial intelligence revolutions – have ushered in a new wave of commodification and disruption, giving rise to digital capitalism. Finally, it discusses the challenges commodification and disruption pose in the form of redistribution of resources, rebalancing of power, rule adaption, and market re-embedding. The paper brings together a wide range of scholarship to offer a historically and theoretically grounded framework for how to think about and study the rise of digital capitalism.
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Personal Responsibility and Beyond: Developing a Comprehensive Conceptualization of Digital Citizenship Competences
As digital transformation reshapes society, it is crucial to understand the evolving demands on education to prepare individuals for this new reality. Although the conceptualizations and objectives vary, digital citizenship education (DCE) typically aims to equip individuals with the competencies necessary not just to thrive in digital and democratic societies but also to critically analyze and actively shape them. However, existing efforts often focus too narrowly on technical skills and online safety, overlooking the broader notion of citizenship in educational contexts. This article addresses this gap by examining the conceptualization of citizenship within the field of DCE, led primarily by the structure of Choi’s (2016) concept analysis, and proposing a more comprehensive framework based on the citizenship ideals by Westheimer and Kahne (2004). Drawing on existing frameworks and synthesizing various DCE approaches, the article presents the Integrated Framework of Abilities for Digital Citizenship (Infra-DC). We then examine existing measurement instruments to determine their alignment with the proposed framework. This conceptual work contributes to advancing DCE efforts by promoting a nuanced understanding of citizenship and providing guidance for future research, program development, and evaluation.
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Polarization and Networked Framing: The Syrian and Ukrainian Refugee Crises on X/Twitter
This exploratory study adopts a mixed-methods approach to examine the dynamics of political communication during refugee crises in German-language X (formerly Twitter), focusing on the Syrian and Ukrainian refugee influxes of September 2015 and March 2022. Using the X API, we collected 551,873 tweets related to the Syrian crisis and 236,034 tweets related to the Ukrainian crisis. The retweet networks associated with both crises were segmented into attitudinal communities by labeling them based on their position toward refugees. These networks were analyzed for polarization, community interactions and activity, influential users, and the dynamic networked framing of the crises. Our social network analysis highlighted that the online anti-refugee community exhibited greater dynamism despite being smaller in size than the pro-refugee community. Elite news media saw a decline in influence, highlighting the lack of intermediary sources between polarized users. While overall networked framing was positive about refugees during both crises, the framing of Ukrainian refugees was more complex and multifaceted. Our results underscore the disrupted state of public discourse on controversial topics and the need to reduce destructive polarization on social media.
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Innovating Democracy? Analyzing the #WirVsVirus Hackathon
The article concerns the case of #WirVsVirus, a civic hackathon organized in reaction to the COVID-19 pandemic and officially endorsed by Germany’s federal government. It aims to address the normative implications of this politically oriented technological format. Specifically, it asks how civic hackathons formulate and negotiate different political representation claims. Our analysis shows that the hackathon constituted a successful representative claim on behalf of civic tech initiatives vis-à-vis the administrative state. While this claim primarily concerned establishing a new format for efficient and subsidiary problem-solving in the wake of the crisis, the hackathon’s participatory promises have only been partially fulfilled. The hackathon was rather open to input from civil society, enabling it to attract substantial public interest. Nonetheless, its technological-organizational structure and competitive, solution-oriented procedures meant that decision-making power remained largely with the hackathon’s organizers.
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Untangling the Future of Diamond Access: Discussing Quality Standards for the Re-communalization of Scholarly Publishing
This paper examines the future of Diamond Open Access as a non-commercial, community-driven model for scholarly publishing that challenges the growing marketization of research dissemination. Drawing on recent initiatives, such as DIAMAS, Craft-OA, the Global Diamond Summits, and ALMASI, it analyzes the existing definitions and standards for Diamond journals, highlighting efforts in Europe and Latin America to establish common criteria for quality, sustainability, and visibility. It emphasizes the need to move beyond universal rankings and impact factors toward context-sensitive federated indexing systems, such as Latindex, SciELO, Redalyc, Biblat, AJOL, or DOAJ, that reflect the diversity of academic communities. The paper argues for a re-communalization of scholarly publishing through institutional support, reliable indexation, and the recognition of multi-indexed journals as legitimate indicators of quality. Ultimately, it proposes reclaiming academic control from corporate infrastructures by reinforcing autonomy, multilingualism, and bibliodiversity in research evaluation and publication practices.
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Educational Impulses for Redesigning (Online) Teaching in the Post-Pandemic World: A Discussion and Evaluation of Lessons Learned
This article reflects on the challenge of online teaching from the perspective of media didactics, a perspective that gained prominence during the COVID-19 pandemic. The qualitative-reconstructive study reflects on 65 multidisciplinary papers written during the pandemic. Together, these studies empirically examine the challenges, achievements, and failures of the first large-scale experiment in university teaching during that time and include quantitative empirical studies and qualitative first-hand accounts from university lectures that document how scholars adapted their courses from on-campus teaching to online teaching. Many approaches are innovative and creative, while some are not really new, at least from the perspective of media education. Still, many teachers with limited exposure to media-based or online teaching pre-pandemic broke new ground in their individual teaching. Of course, learning is an individual process. Nevertheless, expectations that university teaching would be fundamentally redesigned were almost inevitably destined for disappointment due to the pandemic’s suddenness, a lack of didactic knowledge, technical and organizational hurdles, and various other individual challenges. It is now clear that the emergency online semesters have permanently changed university teaching. Learning from both successes and failures, this article proposes the design and development of good (online) teaching for post-pandemic times. It bases its proposals on the documented experiences of teachers, on empirical data, and on three practical examples.
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IDLEWiSE: A Project Concept for AI-Assisted Energy Efficiency in HPC Clusters
The growing energy demand for high-performance computing (HPC) systems raises severe concerns about their environmental impact. Novel system paradigms and computational schemes are needed to limit energy consumption while ensuring the efficiency and availability of computing resources. In this contribution, we introduce a concept for an Intelligent Decision Tool for Lowering Energy Waste in System Efficiency (IDLEWiSE), which aims to decrease the energy consumption of HPC clusters operating below total capacity by selectively shutting down idle computational units. This paper outlines an optimization tool using efficient machine-learning algorithms like decision trees to learn optimal shutdown policies online. We further locate our approach in the context of existing energy-economizing instruments and perform a strategic analysis and stepwise validation of the proposed concept. The study also includes qualitative anonymized findings from a survey of German scientific HPC cluster administrators, corroborating the urgent need for energy-efficient tools and practices for practitioners.
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On Algorithmic Management: The Importance of Debate on Future Research
A surge of research interest in platform work and the gig economy has seen debates around worker resistance and algorithmic management frequently come to the forefront. Many researchers will now be accustomed to reviewing journal submissions and taking in conference papers that cover these issues. The breadth of the emerging literature means that it builds upon various starting points, theoretical approaches, and histories. Pleasingly, research on work over the past decade has transformed from a relatively marginal pursuit to a highly popular focus across many disciplines, deepening and extending our collective understanding of the topic. This has the potential to introduce fresh ideas and new approaches. However, it does risk research failing to relate to and build upon historical debates in the field. This short article first presents some of the key arguments that have emerged in the research on algorithmic management and considers how knowledge has developed in relation to platform work. It examines some of the strengths and weaknesses of the literature in this area, especially the lack of theoretical debate in an exponentially expanding body of literature. The article finishes by suggesting some key areas in which future research needs to be directed, particularly interrogating the production, practice, and limits of algorithmic management.
