arXiv subjects

Christoph Becker

Publications and source records attributed to Christoph Becker.

At least 19 recordsLinked to original sources

arXiv2026

A Scoping Review of Methods to Measure the Energy and Carbon Footprint of Web Tracking and Advertising

The environmental impact of web tracking and advertising is increasingly receiving attention as the ICT sector's carbon footprint keeps rising. Yet the scholarship addressing this question remains scattered across disciplines and inconsistent in its terminology. This paper presents a scoping review of the literature on methods for measuring the energy and carbon footprint of web tracking and advertising. From an initial pool of 46 articles identified through a structured title-based search on Google Scholar, we arrived at a final corpus of 15 papers, from which we identified five distinct methodological approaches: ad blocking, controlled environment, replaying ads, traffic flow analysis, and literature-derived estimation. This review provides a structured overview of the current methodological landscape and a foundation for more comprehensive environmental accounting of the ad tech ecosystem.

cs.CY↗

arXiv2026

Humour as Resistance: Visceralizing the Environmental and Social Impact of AI through Humour-based Creative Practices

The growth of AI does not come without cost. While we hear about the economic costs, the environmental and social costs are often obfuscated by mainstream narratives, and even when acknowledged, are accompanied by a sense of helplessness. To counter this, we, a group of designers and researchers, reflect on our experience leading a humour-based creative campaign surfacing the material impact of AI infrastructures. Through graphics design, physical installations, digital content creation and co-creation workshops, we leaned into humour as a creative practice to provoke collective reflection. Drawing on event ethnography, surveys and interviews with campaign engagers, we examine four roles of humour-based creative work in HCI: a connector to critical friends, a visceral and emotional harbour, social glue, and resistance to power. We argue for the importance of creative practices in bridging social and emotional gaps between people and concerns around technology, and in surfacing hidden perspectives for more inclusive conversations.

cs.HC↗

arXiv2026

Plateau That Never Comes: When Efficiency Claims in Datacenters and AI Become Greenwashing

Datacenter expansion under generative AI is increasingly framed as compatible with sustainability because of efficiency gains, cleaner electricity procurement, and improved facility design. Yet these claims often do not show that absolute electricity, water, material, waste, and community-facing burdens are falling. This Perspective addresses that evidentiary gap. Rather than asking whether efficiency gains are real, we ask when such gains are being enlarged into claims of system-wide sustainability to justify continued expansion. We develop a rebound-informed diagnostic framework for evaluating AI and datacenter sustainability narratives across five tests: metric, boundary, reinvestment, burden shifting, and governance. Applied to major AI industry sustainability reporting, the framework shows that firms largely justify continued expansion through efficiency improvements and clean-energy procurement, rather than by demonstrating reductions in absolute resource use. Applied to plateau claims in the literature, we show that many claims establish local or relative improvements while leaving energy rebound, lifecycle burdens, and enforceable limits unresolved. We argue that these sustainable-growth narratives begin to function as greenwashing when they use efficiency improvements to claim sustainability even as absolute energy, water, material, and public health burdens continue to increase. We conclude by positioning digital sufficiency as a burden-of-proof framework for governance: those advocating further datacenter expansion must show that it reduces, rather than merely redistributes or defers, absolute burdens across the full system.

cs.CY↗

arXiv2026

Relational Aesthesis in Permacomputing Practice: Building a Solar Powered Website from Reclaimed Materials

Permacomputing is a nascent concept and community of practice concerned with developing alternative computing systems grounded in principles of resilience, reuse, sufficiency, and ecological limits. However, research engaging with permacomputing remains in an early stage of development, raising concerns about whether permacomputing can move beyond reflective critique to become a meaningful alternative practice. Through a research-through-design case study, we documented our experience moving a personal website from a data centre in Texas to a self-hosted solar-powered server built from reclaimed electronics. Guided by permacomputing principles and relational aesthesis, we explore what it takes for permacomputing to reconfigure material and perceptual relations. Our findings reveal the frictions of moving away from a maximalist techno-aesthetic while attempting to re-use already existing technologies, potential ways to overcome these challenges through building a community of practice, and the transformative potential of visibilizing and visceralizing digital infrastructures to cultivate more responsible ways of relating to technology. This paper contributes to emerging research on permacomputing and its aesthetics by bringing it into dialogue with theories of non-place and relational aesthesis. Rather than functioning as a purely symbolic gesture, permacomputing practices can cultivate greater collective autonomy, agency, and responsibility in how communities engage and create meaning within digital infrastructures. In the context of socio-ecological crises and anti-colonial transformation, our research offers a situated approach to building and relating to computing technologies in the ashes of dominant technological paradigms.

cs.HC↗

arXiv2026

The Environmental Costs of Surveillance Capitalism: A Case Study of Social Media Platforms

The business model of surveillance capitalism, premised on the extraction of behavioral data and its predictive potential for profit, relies on extensive material infrastructure. Such profit is typically driven by practices such as telemetry, user tracking, data analytics, secondary data uses, increased user engagement, and AI model training, as well as large-scale data storage systems that retain personal information for sale or reuse. This paper is motivated by the question: how much of the rising carbon impact of ICT can be attributed to this material infrastructure? Such an inquiry provides a foundation for quantifying the environmental costs of surveillance capitalism by proposing a conceptual framework and research direction that link processes of surveillance with their underlying material realities. To demonstrate the applicability of this framework, we examine the proportion of network traffic caused by surveillance capitalism processes through a comparative case study of a corporate social media platform, X/formerly Twitter, and a decentralized, non-commercial alternative, Mastodon. Our findings highlight the existence of corporate overhead: excess resource consumption driven by corporate social media practices, which is used as an initial proxy for the activities of surveillance capitalism. Our findings further demonstrate how the corporate overhead of X can be used to establish a lower bound in CO2e emissions attributable to for-profit activities that do not contribute to the user experience.

cs.CY↗

arXiv2026

Evaluating Structured Documentation as a Tool for Reflexivity in Dataset Development

It is prominently recognized that dataset development in machine learning is a value-laden process from problem formulation to data processing, use, and reuse. Structured documentation frameworks such as datasheets, data statements, and dataset nutrition labels have been created to aid developers in documenting how their datasets were produced and, according to the creators of the frameworks, to facilitate reflexivity in dataset development. While reflexivity is a stated goal, it is unclear whether and to what extent these structured dataset documentation frameworks incorporate concepts from reflexivity literature (at FAccT and elsewhere) and whether the use of the frameworks demonstrates reflexivity. Here, we adopt mixed-method thematic analysis and corpus-assisted discourse analysis to explore how reflexivity is incorporated in structured documentation frameworks and their responses. We demonstrate empirically that there is a general lack of engagement with major themes of reflexivity in both dataset documentation frameworks and published applications of these frameworks. We present a codebook of major reflexivity topics, recommend actionable strategies, and propose a set of extended datasheet questions to more effectively incorporate these topics into structured documentation frameworks and in the FAccT literature.

cs.CY↗

arXiv2025

Exploring the Viability of the Updated World3 Model for Examining the Impact of Computing on Planetary Boundaries

The influential Limits to Growth report introduced a system dynamics-based model to demonstrate global dynamics of the world's population, industry, natural resources, agriculture, and pollution between 1900-2100. In current times, the rapidly expanding trajectory of data center development, much of it linked to AI, uses increasing amounts of natural resources. The extraordinary amount of resources claimed warrants the question of how computing trajectories contribute to exceeding planetary boundaries. Based on the general robustness of the World3-03 model and its influence in serving as a foundation for current climate frameworks, we explore whether the model is a viable method to quantitatively simulate the impact of data centers on limits to growth. Our paper explores whether the World3-03 model is a feasible method for reflecting on these dynamics by adding new variables to the model in order to simulate a new AI-augmented scenario. We find that through our addition of AI-related variables (such as increasing data center development) impacting pollution in the World3-03 model, we can observe the expected changes to dynamics, demonstrating the viability of the World3-03 model for examining AI's impact on planetary boundaries. We detail future research opportunities for using the World3-03 model to explore the relationships between increasing resource-intensive computing and the resulting impacts to the environment in a quantitative way given its feasibility.

cs.CY↗

arXiv2025

Limits at a Distance: Design Directions to Address Psychological Distance in Policy Decisions Affecting Planetary Boundaries

Policy decisions relevant to the environment rely on tools like dashboards, risk models, and prediction models to provide information and data visualizations that enable decision-makers to make trade-offs. The conventional paradigm of data visualization practices for policy and decision-making is to convey data in a supposedly neutral, objective manner for rational decision-makers. Feminist critique advocates for nuanced and reflexive approaches that take into account situated decision-makers and their affective relationships to data. This paper sheds light on a key cognitive aspect that impacts how decision-makers interpret data. Because all outcomes from policies relevant to climate change occur at a distance, decision-makers experience so-called `psychological distance' to environmental decisions in terms of space, time, social identity, and hypotheticality. This profoundly impacts how they perceive and evaluate outcomes. Since policy decisions to achieve a safe planetary space are urgently needed for immediate transition and change, we need a design practice that takes into account how psychological distance affects cognition and decision-making. Our paper explores the role of alternative design approaches in developing visualizations used for climate policymaking. We conduct a literature review and synthesis which bridges psychological distance with speculative design and data visceralization by illustrating the value of affective design methods via examples from previous research. Through this work, we propose a novel premise for the communication and visualization of environmental data. Our paper lays out how future research on the impacts of alternative design approaches on psychological distance can make data used for policy decisions more tangible and visceral.

cs.HC↗

arXiv2025

How Viable are Energy Savings in Smart Homes? A Call to Embrace Rebound Effects in Sustainable HCI

As part of global climate action, digital technologies are seen as a key enabler of energy efficiency savings. A popular application domain for this work is smart homes. There is a risk, however, that these efficiency gains result in rebound effects, which reduce or even overcompensate the savings. Rebound effects are well-established in economics, but it is less clear whether they also inform smart energy research in other disciplines. In this paper, we ask: to what extent have rebound effects and their underlying mechanisms been considered in computing, HCI and smart home research? To answer this, we conducted a literature mapping drawing on four scientific databases and a SIGCHI corpus. Our results reveal limited consideration of rebound effects and significant opportunities for HCI to advance this topic. We conclude with a taxonomy of actions for HCI to address rebound effects and help determine the viability of energy efficiency projects.

cs.HC↗

arXiv2025

Limits to AI Growth: The Ecological and Social Consequences of Scaling

The accelerating development and deployment of AI technologies depend on the continued ability to scale their infrastructure. This has implied increasing amounts of monetary investment and natural resources. Frontier AI applications have thus resulted in rising financial, environmental, and social costs. While the factors that AI scaling depends on reach its limits, the push for its accelerated advancement and entrenchment continues. In this paper, we provide a holistic review of AI scaling using four lenses (technical, economic, ecological, and social) and review the relationships between these lenses to explore the dynamics of AI growth. We do so by drawing on system dynamics concepts including archetypes such as "limits to growth" to model the dynamic complexity of AI scaling and synthesize several perspectives. Our work maps out the entangled relationships between the technical, economic, ecological and social perspectives and the apparent limits to growth. The analysis explains how industry's responses to external limits enables continued (but temporary) scaling and how this benefits Big Tech while externalizing social and environmental damages. To avoid an "overshoot and collapse" trajectory, we advocate for realigning priorities and norms around scaling to prioritize sustainable and mindful advancements.

cs.CY↗

arXiv2025

"Near Data" and "Far Data" for Urban Sustainability: How Do Community Advocates Envision Data Intermediaries?

In the densifying data ecosystem of today's cities, data intermediaries are crucial stakeholders in facilitating data access and use. Community advocates live in these sites of social injustices and opportunities for change. Highly experienced in working with data to enact change, they offer distinctive insights on data practices and tools. This paper examines the unique perspectives that community advocates offer on data intermediaries. Based on interviews with 17 advocates working with 23 grassroots and nonprofit organizations, we propose the quality of "near" and "far" to be seriously considered in data intermediaries' works and articulate advocates' vision of connecting "near data" and "far data." To pursue this vision, we identified three pathways for data intermediaries: align data exploration with ways of storytelling, communicate context and uncertainties, and decenter artifacts for relationship building. These pathways help data intermediaries to put data feminism into practice, surface design opportunities and tensions, and raise key questions for supporting the pursuit of the Right to the City.

cs.HC↗

arXiv2024

The State of Data Curation at NeurIPS: An Assessment of Dataset Development Practices in the Datasets and Benchmarks Track

Data curation is a field with origins in librarianship and archives, whose scholarship and thinking on data issues go back centuries, if not millennia. The field of machine learning is increasingly observing the importance of data curation to the advancement of both applications and fundamental understanding of machine learning models - evidenced not least by the creation of the Datasets and Benchmarks track itself. This work provides an analysis of dataset development practices at NeurIPS through the lens of data curation. We present an evaluation framework for dataset documentation, consisting of a rubric and toolkit developed through a literature review of data curation principles. We use the framework to assess the strengths and weaknesses in current dataset development practices of 60 datasets published in the NeurIPS Datasets and Benchmarks track from 2021-2023. We summarize key findings and trends. Results indicate greater need for documentation about environmental footprint, ethical considerations, and data management. We suggest targeted strategies and resources to improve documentation in these areas and provide recommendations for the NeurIPS peer-review process that prioritize rigorous data curation in ML. Finally, we provide results in the format of a dataset that showcases aspects of recommended data curation practices. Our rubric and results are of interest for improving data curation practices broadly in the field of ML as well as to data curation and science and technology studies scholars studying practices in ML. Our aim is to support continued improvement in interdisciplinary research on dataset practices, ultimately improving the reusability and reproducibility of new datasets and benchmarks, enabling standardized and informed human oversight, and strengthening the foundation of rigorous and responsible ML research.

cs.CY↗

arXiv2024

Machine Learning Data Practices through a Data Curation Lens: An Evaluation Framework

Studies of dataset development in machine learning call for greater attention to the data practices that make model development possible and shape its outcomes. Many argue that the adoption of theory and practices from archives and data curation fields can support greater fairness, accountability, transparency, and more ethical machine learning. In response, this paper examines data practices in machine learning dataset development through the lens of data curation. We evaluate data practices in machine learning as data curation practices. To do so, we develop a framework for evaluating machine learning datasets using data curation concepts and principles through a rubric. Through a mixed-methods analysis of evaluation results for 25 ML datasets, we study the feasibility of data curation principles to be adopted for machine learning data work in practice and explore how data curation is currently performed. We find that researchers in machine learning, which often emphasizes model development, struggle to apply standard data curation principles. Our findings illustrate difficulties at the intersection of these fields, such as evaluating dimensions that have shared terms in both fields but non-shared meanings, a high degree of interpretative flexibility in adapting concepts without prescriptive restrictions, obstacles in limiting the depth of data curation expertise needed to apply the rubric, and challenges in scoping the extent of documentation dataset creators are responsible for. We propose ways to address these challenges and develop an overall framework for evaluation that outlines how data curation concepts and methods can inform machine learning data practices.

cs.CY↗

arXiv2023

Quantum Gate Optimization for Rydberg Architectures in the Weak-Coupling Limit

We demonstrate machine learning assisted design of a two-qubit gate in a Rydberg tweezer system. Two low-energy hyperfine states in each of the atoms represent the logical qubit and a Rydberg state acts as an auxiliary state to induce qubit interaction. Utilizing a hybrid quantum-classical optimizer, we generate optimal pulse sequences that implement a CNOT gate with high fidelity, for experimentally realistic parameters and protocols, as well as realistic limitations. We show that local control of single qubit operations is sufficient for performing quantum computation on a large array of atoms. We generate optimized strategies that are robust for both the strong-coupling, blockade regime of the Rydberg states, but also for the weak-coupling limit. Thus, we show that Rydberg-based quantum information processing in the weak-coupling limit is a desirable approach, being robust and optimal, with current technology.

quant-ph↗

arXiv2023

Beyond Transactional Democracy: A Study of Civic Tech in Canada

Technologies are increasingly enrolled in projects to involve civilians in the work of policy-making, often under the label of 'civic technology'. But conventional forms of participation through transactions such as voting provide limited opportunities for engagement. In response, some civic tech groups organize around issues of shared concern to explore new forms of democratic technologies. How does their work affect the relationship between publics and public servants? This paper explores how a Civic Tech Toronto creates a platform for civic engagement through the maintenance of an autonomous community for civic engagement and participation that is casual, social, nonpartisan, experimental, and flexible. Based on two years of action research, including community organizing, interviews, and observations, this paper shows how this grassroots civic tech group creates a civic platform that places a diverse range of participants in contact with the work of public servants, helping to build capacities and relationships that prepare both publics and public servants for the work of participatory democracy. The case shows that understanding civic tech requires a lens beyond the mere analysis or production of technical artifacts. As a practice for making technologies that is social and participatory, civic tech creates alternative modes of technology development and opportunities for experimentation and learning, and it can reconfigure the roles of democratic participants.

cs.CY↗

arXiv2020

Interorbital Interactions in an SU(2)xSU(6)-Symmetric Fermi-Fermi Mixture

We characterize inter- and intraisotope interorbital interactions between atoms in the 1S0 ground state and the 3P0 metastable state in interacting Fermi-Fermi mixtures of 171Yb and 173Yb. We perform high-precision clock spectroscopy to measure interaction-induced energy shifts in a deep 3D optical lattice and determine the corresponding scattering lengths. We find the elastic interaction of the interisotope mixtures 173Yb_e-171Yb_g and 173Yb_g-171Yb_e to be weakly attractive and very similar, while the corresponding two-body loss coefficients differ by more than two orders of magnitude. By comparing different spin mixtures we experimentally demonstrate the SU(2)xSU(6) symmetry of all elastic and inelastic interactions. Furthermore, we measure the spin-exchange interaction in 171Yb and confirm its previously observed antiferromagnetic nature.

cond-mat.quant-gas↗

arXiv2020

Proca-stinated Cosmology II: Matter, Halo, and Lensing Statistics in the vector Galileon

The generalised Proca (GP) theory is a modified gravity model in which the acceleration of the cosmic expansion rate can be explained by self interactions of a cosmological vector field. In this paper we study a particular sub-class of the GP theory, with up to cubic order Lagrangian, known as the cubic vector Galileon (cvG) model. This model is similar to the cubic scalar Galileon (csG) in many aspects, including a fifth force and the Vainshtein screening mechanism, but with the additional flexibility that the strength of the fifth force depends on an extra parameter -- interpolating between zero and the full strength of the csG model -- while the background expansion history is independent of this parameter. It offers an interesting alternative to LambdaCDM in explaining the cosmic acceleration, as well as a solution to the tension between early- and late-time measurements of the Hubble constant H_0. To identify the best ways to test this model, in this paper we conduct a comprehensive study of the phenomenology of this model in the nonlinear regime of large-scale structure formation, using a suite of N-body simulations run with the modified gravity code ECOSMOG. By inspecting thirteen statistics of the dark matter field, dark matter haloes and weak lensing maps, we find that the fifth force in this model can have particularly significant effects on the large-scale velocity field and lensing potential at late times, which suggest that redshift-space distortions and weak lensing can place strong constraints on it.

astro-ph.CO↗

arXiv2020

Proca-stinated Cosmology I: A N-body code for the vector Galileon

We investigate the nonlinear growth of large-scale structure in the generalised Proca theory, in which a self-interacting massive vector field plays the role of driving the acceleration of the cosmic expansion. Focusing to the Proca Lagrangian at cubic order -- the cubic vector Galileon model -- we derive the simplified equations for gravity as well as the longitudinal and transverse modes of the vector field under the weak-field and quasi-static approximations, and implement them in a modified version of the ECOSMOG N-body code. Our simulations incorporate the Vainshtein screening effect, which reconciles the fifth force propagated by the longitudinal mode of the cubic vector Galileon model with local tests of gravity. The results confirm that for all scales probed by the simulation, the transverse mode has a negligible impact on structure formation in a realistic cosmological setup. It is well known that in this model the strength of the fifth force is controlled by a free model parameter, which we denote as beta_3. By running a suite of cosmological simulations for different values of beta_3, we show that this parameter also determines the effectiveness of the Vainshtein screening. The model behaves identically to the cubic scalar Galileon for beta_3 going to zero, in which the fifth force is strong in unscreened regions but is efficiently screened in high-density regions. In the opposite limit, beta_3 going to infinity, the model approaches its `quintessence' counterpart, which has a vanishing fifth force but a modified expansion history compared to LambdaCDM. This endows the model with rich phenomenology, which will be investigated in future works.

astro-ph.CO↗