Paper by Brandon Reynante, Steven P. Dow, Narges Mahyar: “Civic problems are often too complex to solve through traditional top-down strategies. Various governments and civic initiatives have explored more community-driven strategies where citizens get involved with defining problems and innovating solutions. While certain people may feel more empowered, the public at large often does not have accessible, flexible, and meaningful ways to engage. Prior theoretical frameworks for public participation typically offer a one-size-fits-all model based on face-to-face engagement and fail to recognize the barriers faced by even the most engaged citizens. In this article, we explore a vision for open civic design where we integrate theoretical frameworks from public engagement, crowdsourcing, and design thinking to consider the role technology can play in lowering barriers to large-scale participation, scaffolding problem-solving activities, and providing flexible options that cater to individuals’ skills, availability, and interests. We describe our novel theoretical framework and analyze the key goals associated with this vision: (1) to promote inclusive and sustained participation in civics; (2) to facilitate effective management of large-scale participation; and (3) to provide a structured process for achieving effective solutions. We present case studies of existing civic design initiatives and discuss challenges, limitations, and future work related to operationalizing, implementing, and testing this framework…(More)”.
Surveillance Publishing
Working paper by Jefferson D. Pooley: “…This essay lingers on a prediction too: Clarivate’s business model is coming for scholarly publishing. Google is one peer, but the company’s real competitors are Elsevier, Springer Nature, Wiley, Taylor & Francis, and SAGE. Elsevier, in particular, has been moving into predictive analytics for years now. Of course the publishing giants have long profited off of academics and our university employers— by packaging scholars’ unpaid writing-and-editing labor only to sell it back to us as usuriously priced subscriptions or APCs. That’s a lucrative business that Elsevier and the others won’t give up. But they’re layering another business on top of their legacy publishing operations, in the Clarivate mold. The data trove that publishers are sitting on is, if anything, far richer than the citation graph alone. Why worry about surveillance publishing? One reason is the balance-sheet, since the companies’ trading in academic futures will further pad profits at the expense of taxpayers and students. The bigger reason is that our behavior—once alienated from us and abstracted into predictive metrics—will double back onto our work lives. Existing biases, like male academics’ propensity for selfcitation, will receive a fresh coat of algorithmic legitimacy. More broadly, the academic reward system is already distorted by metrics. To the extent that publishers’ tallies and indices get folded into grant-making, tenure-and-promotion, and other evaluative decisions, the metric tide will gain power. The biggest risk is that scholars will internalize an analytics mindset, one already encouraged by citation counts and impact factors….(More)”.
The AI Carbon Footprint and Responsibilities of AI Scientists
Paper by Guglielmo Tamburrini: “This article examines ethical implications of the growing AI carbon footprint, focusing on the fair distribution of prospective responsibilities among groups of involved actors. First, major groups of involved actors are identified, including AI scientists, AI industry, and AI infrastructure providers, from datacenters to electrical energy suppliers. Second, responsibilities of AI scientists concerning climate warming mitigation actions are disentangled from responsibilities of other involved actors. Third, to implement these responsibilities nudging interventions are suggested, leveraging on AI competitive games which would prize research combining better system accuracy with greater computational and energy efficiency. Finally, in addition to the AI carbon footprint, it is argued that another ethical issue with a genuinely global dimension is now emerging in the AI ethics agenda. This issue concerns the threats that AI-powered cyberweapons pose to the digital command, control, and communication infrastructure of nuclear weapons systems…(More)”.
Citizen Power Europe. The Making of a European Citizens’ Assembly
Paper by Alberto Alemanno and Kalypso Nicolaïdis: “This article argues that if the EU is to recover its dented popularity among European publics, we need to build a European democratic ecosystem to nurture, scale and ultimate accommodate the daily competing claims of Europe’s citizens. To attain this objective, it presents and discusses three big ideas that are at the heart of the renewed EU ecosystem that we are calling for. These are: participation beyond voting; a transnational and inclusive public space; and, a democratic panopticon for greater accountability. Promisingly enough, these ideas already find reflection in the first batch of the citizens’ recommendations emerging from the Conference on the Future of Europe (CoFoE). Even if these recommendations still need to be refined through deliberation by the plenary of the CoFoE, they add up a clear and urgent message: let’s tap into our collective intelligence and democratic imagination to construct a pan-European public sphere by enhancing mutual connections, knowledge and empowerment between citizens across borders…(More)”.
Smart Cities: Mapping their Ethical Implications
Paper by Marta Ziosi, Benjamin Hewitt, Prathm Juneja, Mariarosaria Taddeo, and Luciano Floridi: “This paper provides an overview of the various definitions and labels attached to the concept of smart cities in order to identify four dimensions that ground the analysis of ethical concerns emerging from the current debate. These are: (1) network infrastructure, with the corresponding concerns of control, surveillance, and data privacy and ownership; (2) post-political governance, embodied in the tensions between public and private decision-making and cities as post-political entities; (3) social inclusion, expressed in the aspects of citizen participation and inclusion, and inequality and discrimination; and (4) sustainability, with a specific focus on the environment as an element to protect but also as a strategic component for the future. Notwithstanding the persisting disagreements around the definition of a smart city, the article uses these four dimensions to analyse both the different types and conceptions of smart cities and the multiple aspects in which smart cities reinforce old and introduce new ethical problems…(More)“
A data-based participatory approach for health equity and digital inclusion: prioritizing stakeholders
Paper by Aristea Fotopoulou, Harriet Barratt, and Elodie Marandet: “This article starts from the premise that projects informed by data science can address social concerns, beyond prioritizing the design of efficient products or services. How can we bring the stakeholders and their situated realities back into the picture? It is argued that data-based, participatory interventions can improve health equity and digital inclusion while avoiding the pitfalls of top-down, technocratic methods. A participatory framework puts users, patients and citizens as stakeholders at the centre of the process, and can offer complex, sustainable benefits, which go beyond simply the experience of participation or the development of an innovative design solution. A significant benefit for example is the development of skills, which should not be seen as a by-product of the participatory processes, but a central element of empowering marginalized or excluded communities to participate in public life. By drawing from different examples in various domains, the article discusses what can be learnt from implementations of schemes using data science for social good, human-centric design, arts and wellbeing, to argue for a data-centric, creative and participatory approach to address health equity and digital inclusion in tandem…(More)”.
Toward A Collaborative Smart City: A Play-Based Urban Living Laboratory in Boston
Paper by Eric Gordon, John Harlow, Melissa Teng & Elizabeth Christoferetti: This article reports on an urban living laboratory that designed a suite of play-based prototypes, as an attempt to “institution” collaborative smart city governance in the city of Boston. This project was called “Beta Blocks,” and it geographically defined “Exploration Zones,” governed by local residents and business owners, who decided whether, where, and why to temporarily install technologies in the public realm. To recruit and facilitate the participation of Zone Advisory Group members, the authors fabricated a lavender, parking-space-sized, inflatable art exhibition (Beta Blob) that hosted a suite of public-facing activities. Although the composite model failed at “institutioning” itself into Boston’s government through this prototype, the discrete components succeeded in centering play in public learning situations and prototyping a model for collaborative governance between publics, and the public and private sectors…(More)”.
Incentivising research data sharing: a scoping review
Paper by Helen Buckley Woods and Stephen Pinfield: “Numerous mechanisms exist to incentivise researchers to share their data. This scoping review aims to identify and summarise evidence of the efficacy of different interventions to promote open data practices and provide an overview of current research….Seven major themes in the literature were identified: publisher/journal data sharing policies, metrics, software solutions,research data sharing agreements in general, open science ‘badges’, funder mandates, and initiatives….
A number of key messages for data sharing include: the need to build on existing cultures and practices, meeting people where they are and tailoring interventions to support them; the importance of publicising and explaining the policy/service widely; the need to have disciplinary data champions to model good practice and drive cultural change; the requirement to resource interventions properly; and the imperative to provide robust technical infrastructure and protocols, such as labelling of data sets, use of DOIs, data standards and use of data repositories….(More)”.
Biases in human mobility data impact epidemic modeling
Paper by Frank Schlosser, Vedran Sekara, Dirk Brockmann, and Manuel Garcia-Herranz: “Large-scale human mobility data is a key resource in data-driven policy making and across many scientific fields. Most recently, mobility data was extensively used during the COVID-19 pandemic to study the effects of governmental policies and to inform epidemic models. Large-scale mobility is often measured using digital tools such as mobile phones. However, it remains an open question how truthfully these digital proxies represent the actual travel behavior of the general population. Here, we examine mobility datasets from multiple countries and identify two fundamentally different types of bias caused by unequal access to, and unequal usage of mobile phones. We introduce the concept of data generation bias, a previously overlooked type of bias, which is present when the amount of data that an individual produces influences their representation in the dataset. We find evidence for data generation bias in all examined datasets in that high-wealth individuals are overrepresented, with the richest 20% contributing over 50% of all recorded trips, substantially skewing the datasets. This inequality is consequential, as we find mobility patterns of different wealth groups to be structurally different, where the mobility networks of high-wealth users are denser and contain more long-range connections. To mitigate the skew, we present a framework to debias data and show how simple techniques can be used to increase representativeness. Using our approach we show how biases can severely impact outcomes of dynamic processes such as epidemic simulations, where biased data incorrectly estimates the severity and speed of disease transmission. Overall, we show that a failure to account for biases can have detrimental effects on the results of studies and urge researchers and practitioners to account for data-fairness in all future studies of human mobility…(More)”.
Expecting the Unexpected: Effects of Data Collection Design Choices on the Quality of Crowdsourced User-Generated Content
Paper by Roman Lukyanenko: “As crowdsourced user-generated content becomes an important source of data for organizations, a pressing question is how to ensure that data contributed by ordinary people outside of traditional organizational boundaries is of suitable quality to be useful for both known and unanticipated purposes. This research examines the impact of different information quality management strategies, and corresponding data collection design choices, on key dimensions of information quality in crowdsourced user-generated content. We conceptualize a contributor-centric information quality management approach focusing on instance-based data collection. We contrast it with the traditional consumer-centric fitness-for-use conceptualization of information quality that emphasizes class-based data collection. We present laboratory and field experiments conducted in a citizen science domain that demonstrate trade-offs between the quality dimensions of accuracy, completeness (including discoveries), and precision between the two information management approaches and their corresponding data collection designs. Specifically, we show that instance-based data collection results in higher accuracy, dataset completeness and number of discoveries, but this comes at the expense of lower precision. We further validate the practical value of the instance-based approach by conducting an applicability check with potential data consumers (scientists, in our context of citizen science). In a follow-up study, we show, using human experts and supervised machine learning techniques, that substantial precision gains on instance-based data can be achieved with post-processing. We conclude by discussing the benefits and limitations of different information quality and data collection design choice for information quality in crowdsourced user-generated content…(More)”.