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.

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.

The use of ICT tools and platforms to support the financial practices of underserved communities has been growing, but on-ground experiences have been mixed and sometimes detrimental. To achieve the potential benefits of technologies for financial inclusion, a deeper understanding of the design of digital financial interventions is essential. We systematically and critically reviewed studies that developed and implemented technological interventions for financial inclusion from 2007 to 2024. Our analysis examines the types of financial technologies developed, the devices and technical channels used, the research methodologies employed, and the target populations. Additionally, we conducted a reflexive thematic analysis to investigate the values underpinning these studies. By analyzing 30 articles, we interpreted a total of 13 value themes-including trust, accessibility, robustness, and compatibility. Our work highlights a complex interplay of values, structural considerations, technical attributes, financial practices, and economic factors that underlie the design of financial technologies.

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 recent 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 thorough literature review of data curation principles. We use the framework to systematically 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. We also provide guidelines for dataset developers on the use of our rubric as a standalone tool. 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.

Decision making pervades software and systems engineering. Intertemporal decisions involve trade-offs among outcomes at different points in time. They play a central role in systems design, as recognised since the inception of the software engineering (SE) field. They are also crucial for the sustainability of design decisions. However, temporal decision making is not adequately understood in SE. The field of Judgement and Decision Making (JDM) offers important empirical findings and research methods that could be utilised. This article establishes a baseline for studying how software professionals handle intertemporal choices. It examines how temporal distance affects choices in an example scenario, explores in what areas of software development such decisions can be found, and examines how systems design decisions can be characterised and studied as intertemporal. We developed a method to study intertemporal choice in SE, based on an initial set of psychological theory grounded in JDM. We instantiated the method in a study to elicit responses to an intertemporal choice task followed by a Cognitive Task Analysis (CTA) interview. We found that study participants overall tended to discount future outcomes, but individual participants varied wildly in how they valued present vs. future outcomes. They indicated several locations in which intertemporal choices occur in everyday software development. Based on these findings, and by reconciling our initial theory with existing JDM theory and results, we further developed and refined our theory and study method into a framework for studying intertemporal decision making in SE. To obtain a basis for more sustainable software systems design decisions, SE research should adopt a more comprehensive, detailed, and empirically consistent way of understanding and studying intertemporal choices. We provide suggestions for how future research could achieve practical methods that address essential characteristics of real-life systems design decisions.

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.

To understand and improve their current abilities and maturity, organizations use diagnostic instruments such as maturity models and other assessment frameworks. Increasing numbers of these are being developed in digital curation. Their central role in strategic decision making raises the need to evaluate their fitness for this purpose and develop guidelines for their design and evaluation. A comprehensive review of assessment frameworks, however, found little evidence that existing assessment frameworks have been evaluated systematically, and no methods for their evaluation. This article proposes a new methodology for evaluating the design and use of assessment frameworks. It builds on prior research on maturity models and combines analytic and empirical evaluation methods to explain how the design of assessment frameworks influences their application in practice, and how the design process can effectively take this into account. We present the evaluation methodology and its application to two frameworks. The evaluation results lead to guidelines for the design process of assessment frameworks in digital curation. The methodology provides insights to the designers of the evaluated frameworks that they can consider in future revisions; methodical guidance for researchers in the field; and practical insights and words of caution to organizations keen on diagnosing their abilities.

Background: Many decisions made in Software Engineering practices are intertemporal choices: trade-offs in time between closer options with potential short-term benefit and future options with potential long-term benefit. However, how software professionals make intertemporal decisions is not well understood. Aim: This paper investigates how shifting time frames influence preferences in software projects in relation to purposefully selected background factors. Method: We investigate temporal discounting by replicating a questionnaire-based observational study. The replication uses a changed-population and -experimenter design to increase the internal and external validity of the original results. Results: The results of this study confirm the occurrence of temporal discounting in samples of both professional and student participants from different countries and demonstrate strong variance in discounting between study participants. We found that professional experience influenced discounting. Participants with broader professional experience exhibited less discounting than those with narrower experience. Conclusions: The results provide strong empirical support for the relevance and importance of temporal discounting in SE and the urgency of targeted interdisciplinary research to explore the underlying mechanisms and their theoretical and practical implications. The results suggest that technical debt management could be improved by increasing the breadth of experience available for critical decisions with long-term impact. In addition, the present study provides a methodological basis for replicating temporal discounting studies in software engineering.

Technical Debt management decisions always imply a trade-off among outcomes at different points in time. In such intertemporal choices, distant outcomes are often valued lower than close ones, a phenomenon known as temporal discounting. Technical Debt research largely develops prescriptive approaches for how software engineers should make such decisions. Few have studied how they actually make them. This leaves open central questions about how software practitioners make decisions. This paper investigates how software practitioners discount uncertain future outcomes and whether they exhibit temporal discounting. We adopt experimental methods from intertemporal choice, an active area of research. We administered an online questionnaire to 33 developers from two companies in which we presented choices between developing a feature and making a longer-term investment in architecture. The results show wide-spread temporal discounting with notable differences in individual behavior. The results are consistent with similar studies in consumer behavior and raise a number of questions about the causal factors that influence temporal discounting in software engineering. As the first empirical study on intertemporal choice in SE, the paper establishes an empirical basis for understanding how software developers approach intertemporal choice and provides a blueprint for future studies.

Technical Debt arises from decisions that favour short-term outcomes at the cost of longer-term disadvantages. They may be taken knowingly or based on missing or incomplete awareness of the costs; they are taken in different roles, situations, stages and ways. Whatever technical or business factor motivate such decisions, they always imply a trade-off in time, a 'now vs. later'. How exactly are such decisions made, and how have they been studied?

Requirements Engineering (RE) plays a critical role in software system development and is argued to be the key leverage point for practitioners who want to design sustainable software-intensive systems. However, existing RE methods and tools do not explicitly facilitate the discussion and negotiation of sustainability-related concerns. This leads to insufficient or onedimensional perceptions of sustainability. In this paper, we discuss our understanding of sustainability and its relationship with requirements. Based on the outcomes of this discussion, we have extended the WinWin Negotiation Model by incorporating sustainability concepts so that the negotiation also includes the ability to consider the impact of requirements on sustainability. Applying this negotiation method in an exploratory industrial case study, we have learned that this approach stimulates the discussion on sustainability and its multiple dimensions. It also allows practitioners to reflect on requirements and their effects on sustainability. However, we have also observed that further in-depth requirements analysis is needed to analyse the long-term effects of requirements regarding sustainability.

Continuous requirements elicitation is an essential aspect of software product evolution to keep systems aligned with changing user needs. However, current requirements engineering approaches do not explicitly address sustainability in the evolution of systems. Reasons include a lack of awareness and a lack of shared understanding of the concept of sustainability in the RE community. Identifying and analysing the effects of requirements regarding sustainability is challenging, as these effects can have an impact on multiple stakeholders and manifest themselves in one or more sustainability dimensions at different points in time. We argue that crowd-focused semi-automated requirements engineering allows the engagement of a large number of stakeholders (including users and domain experts) in a continuous cycle of negotiation regarding the potential effects of requirements on sustainability. Based on a motivating scenario, we introduce the idea of a platform for crowd-focused requirements engineering that supports the evolution towards sustainability. For the three key aspects of this platform, we present our ongoing work and discuss early results. We outline how the platform can be utilised to improve the broader awareness and understanding of sustainability, not only for the involved crowd but also for researchers and society in general.

The increasing use and prominence of web archives raises the urgency of establishing mechanisms for transparency in the making of web archives to facilitate the process of evaluating a web archive's provenance, scoping, and absences. Some choices and process events are captured automatically, but their interactions are not currently well understood or documented. This study examined the decision space of web archives and its role in shaping what is and what is not captured in the web archiving process. By comparing how three different web archives collections were created and documented, we investigate how curatorial decisions interact with technical and external factors and we compare commonalities and differences. The findings reveal the need to understand both the social and technical context that shapes those decisions and the ways in which these individual decisions interact. Based on the study, we propose a framework for documenting key dimensions of a collection that addresses the situated nature of the organizational context, technical specificities, and unique characteristics of web materials that are the focus of a collection. The framework enables future researchers to undertake empirical work studying the process of creating web archives collections in different contexts.

The lifecycles of format technology have been a defining concern for digital stewardship research and practice. However, little evidence exists to provide robust methods for assessing the state of any given format technology and describing its evolution over time. This article introduces relevant models from diffusion theory and market research and presents a replicable analysis method to compute models of technology evolution. Data cleansing and the combination of multiple data sources enable the application of nonlinear regression to estimate the parameters of the Bass diffusion model on format technology market lifecycles. Through its application to a longitudinal data set from the UK Web Archive, we demonstrate that the method produces reliable results and show that the Bass model can be used to describe format lifecycles. By analyzing adoption patterns across market segments, new insights are inferred about how the diffusion of formats and products such as applications occurs over time. The analysis provides a stepping stone to a more robust and evidence‐based approach to model technology evolution.

As the field of digital preservation (DP) matures, there is an increasing need to systematically assess an organization's abilities to achieve its digital preservation goals, and a wide variety of assessment tools have been created for this purpose. This article aims to map the landscape of research in this area, evaluate the current maturity of knowledge on this central question in DP and provide direction for future research. To do so, this paper reviews assessment frameworks in digital preservation through a systematic literature search and categorizes the literature by type of research. The analysis shows that publication output around assessment in digital preservation has increased markedly over time, but most existing work focuses on developing new models rather than rigorous evaluation and validation of existing frameworks. Significant gaps are present in the application of robust conceptual foundations and design methods, and in the level of empirical evidence available to enable the evaluation and validation of assessment models. The analysis and comparison with other fields suggest that the design of assessment models in DP should be studied rigorously in both theory and practice, and that the development of future models will benefit from applying existing methods, processes, and principles for model design.

Use of computational methods for exploration and analysis of web archives sources is emerging in new disciplines such as digital humanities. This raises urgent questions about how such research projects process web archival material using computational methods to construct their findings. This paper aims to enable web archives scholars to document their practices systematically to improve the transparency of their methods. We adopt the Research Object framework to characterize three case studies that use computational methods to analyze web archives within digital history research. We then discuss how the framework can support the characterization of research methods and serve as a basis for discussions of methods and issues such as reuse and provenance. The results suggest that the framework provides an effective conceptual perspective to describe and analyze the computational methods used in web archive research on a high level and make transparent the choices made in the process. The documentation of the research process contributes to a better understanding of the findings and their provenance, and the possible reuse of data, methods, and workflows.

Sustainability has emerged as a broad concern for society. Many engineering disciplines have been grappling with challenges in how we sustain technical, social and ecological systems. In the software engineering community, for example, maintainability has been a concern for a long time. But too often, these issues are treated in isolation from one another. Misperceptions among practitioners and research communities persist, rooted in a lack of coherent understanding of sustainability, and how it relates to software systems research and practice. This article presents a cross-disciplinary initiative to create a common ground and a point of reference for the global community of research and practice in software and sustainability, to be used for effectively communicating key issues, goals, values and principles of sustainability design for software-intensive systems.The centrepiece of this effort is the Karlskrona Manifesto for Sustainability Design, a vehicle for a much needed conversation about sustainability within and beyond the software community, and an articulation of the fundamental principles underpinning design choices that affect sustainability. We describe the motivation for developing this manifesto, including some considerations of the genre of the manifesto as well as the dynamics of its creation. We illustrate the collaborative reflective writing process and present the current edition of the manifesto itself. We assess immediate implications and applications of the articulated principles, compare these to current practice, and suggest future steps.

The enormous amount of valuable information that is produced today and needs to be made available over the long-term has led to increased efforts in scalable, automated solutions for long-term digital preservation. The mission of preservation planning is to define the optimal actions to ensure future access to digital content and react to changes that require adjustments in repository operations. Considerable effort has been spent in the past on defining, implementing, and validating a framework and system for preservation planning. This article sheds light on the actual decision criteria and influence factors to be considered when choosing digital preservation actions. It is based on an extensive evaluation of case studies on preservation planning for a range of different types of objects with partners from different institutional backgrounds. We categorize decision criteria from a number of real-world decision-making instances in a taxonomy. We show that a majority of the criteria can be evaluated by applying automated measurements under realistic conditions, and demonstrate that controlled experimentation and automated measurements can be used to substantially improve repeatability of decisions and reduce the effort needed to evaluate preservation components. The presented measurement framework enables scalable preservation and monitoring and supports trust in preservation decisions because extensive evidence is produced in a reproducible, automated way and documented as the basis of decision making in a standardized form.