Paper by Louise Mc Grath-Lone et al: “Administrative data are a valuable research resource, but are under-utilised in the UK due to governance, technical and other barriers (e.g., the time and effort taken to gain secure data access). In recent years, there has been considerable government investment in making administrative data “research-ready”, but there is no definition of what this term means. A common understanding of what constitutes research-ready administrative data is needed to establish clear principles and frameworks for their development and the realisation of their full research potential…Overall, we screened 2,375 records and identified 38 relevant studies published between 2012 and 2021. Most related to administrative data from the UK and US and particularly to health data. The term research-ready was used inconsistently in the literature and there was some conflation with the concept of data being ready for statistical analysis. From the thematic analysis, we identified five defining characteristics of research-ready administrative data: (a) accessible, (b) broad, (c) curated, (d) documented and (e) enhanced for research purposes…
Our proposed characteristics of research-ready administrative data could act as a starting point to help data owners and researchers develop common principles and standards. In the more immediate term, the proposed characteristics are a useful framework for cataloguing existing research-ready administrative databases and relevant resources that can support their development…(More)”.
Tech Inclusion for Excluded Communities
Essay by Linda Jakob Sadeh & Smadar Nehab: “Companies often offer practical trainings to address the problem of diversity in high tech, acknowledging the disadvantages that members of excluded communities face and trying to level the playing field in terms of expertise and skills. But such trainings often fail in generating mass participation among excluded communities in tech professions. Beyond the professional knowledge and hands-on technical experience that these trainings provide, the fundamental social, ethnic, and economic barriers often remain unaddressed.
Thus, a paradoxical situation arises: On the one hand, certain communities are excluded from high tech and from the social mobility it affords. On the other hand, even when well-meaning companies wish to hire from these communities and implement diversity and inclusion measures that should make doing so possible, the pool of qualified and interested candidates often remains small. Members of the excluded communities remain discouraged from studying or training for these professions and from joining economic growth sectors, particularly high tech.
Tech Inclusion, the model we advance in this article, seeks to untangle this paradox. It takes a sincere look at the social and economic barriers that prevent excluded communities from participating in the tech industry. It suggests that the technology industry can be a driving force for inclusion if we turn the inclusion paradigm on its head, by bringing the industry to the excluded community, instead of trying to bring the excluded community to the industry, while cultivating a supportive environment for both potential candidates and firms…(More)”.
A Theory of Visionary Disruption
Paper by Joshua S. Gans: “Exploitation of disruptive technologies often requires resource deployment that creates conflict if there are divergent beliefs regarding the efficacy of a new technology. This arises when a visionary agent has more optimistic beliefs about a technological opportunity. Exploration in the form of experiments can be persuasive when beliefs differ by mitigating disagreement and its costs. This paper examines experimental choice when experiments need to persuade as well as inform. It is shown that, due to resource constraints, persuasion factors more highly for entrepreneurial than incumbent firms. However, incumbent firms, despite being able to redeploy resources using authority, are constrained in adoption as exploration cannot mitigate the costs of disagreement…(More)”.
Smart Cities and Smart Communities: Empowering Citizens through Intelligent Technologies
Book edited by Srikanta Patnaik, Siddhartha Sen, Sudeshna Ghosh: “Smart City” programs and strategies have become one of the most dominant urban agendas for local governments worldwide in the past two decades. The rapid urbanization rate and unprecedented growth of megacities in the 21st century triggered drastic changes in traditional ways of urban policy and planning, leading to an influx of digital technology applications for fast and efficient urban management. With the rising popularity in making our cities “smart”, several domains of urban management, urban infrastructure, and urban quality-of-life have seen increasing dependence on advanced information and communication technologies (ICTs) that optimize and control the day-to-day functioning of urban systems. Smart Cities, essentially, act as digital networks that obtain large-scale real-time data on urban systems, process them, and make decisions on how to manage them efficiently. The book presents 26 chapters, which are organized around five topics: (1) Conceptual framework for smart cities and communities; (2) Technical concepts and models for smart city and communities; (3) Civic engagement and citizen participation; (4) Case studies from the Global North; and (5) Case studies from the Global South…(More)”.
Collective Intelligence for Smart Cities
Book by Chun HO WU, George To Sum Ho, Fatos Xhafa, Andrew W. H. IP, Reinout Van Hille: “Collective Intelligence for Smart Cities begins with an overview of the fundamental issues and concepts of smart cities. Surveying the current state-of-the-art research in the field, the book delves deeply into key smart city developments such as health and well-being, transportation, safety, energy, environment and sustainability. In addition, the book focuses on the role of IoT cloud computing and big data, specifically in smart city development. Users will find a unique, overarching perspective that ties together these concepts based on collective intelligence, a concept for quantifying mass activity familiar to many social science and life science researchers. Sections explore how group decision-making emerges from the consensus of the collective, collaborative and competitive activities of many individuals, along with future perspectives…(More)”
The Sky’s Not The Limit: How Lower-Income Cities Can Leverage Drones
Report by UNDP: “Unmanned aerial vehicles (UAVs) are playing an important role in last-mile service delivery around the world. However, COVID-19 has highlighted a potentially broader role that UAVs could play – in cities. Higher-income cities are exploring the technology, but there is little documentation of use cases or potential initiatives in a development context. This report provides practical and applied guidance to lower-income cities looking to explore how drones can support key urban objectives…(More)”.
The Need for New Methods to Establish the Social License for Data Reuse
Stefaan G. Verhulst & Sampriti Saxena at Data & Policy: “Data has rapidly emerged as an invaluable asset in societies and economies, leading to growing demands for innovative and transformative data practices. One such practice that has received considerable attention is data reuse. Data reuse is at the forefront of an emerging “third wave of open data” (Verhulst et al., 2020). Data reuse takes place when data collected for one purpose is used subsequently for an alternative purpose, typically with the justification that such secondary use has potential positive social impact (Choo et al., 2021). Since data is considered a non-rivalrous good, it can be used an infinite number of times, each use potentially bringing new insights and solutions to public problems (OECD, 2021). Data reuse can also lead to lower project costs and more sustainable outcomes for a variety of data-enabled initiatives across sectors.
A social license, or social license to operate, captures multiple stakeholders’ acceptance of standard practices and procedures (Kenton, 2021). Stakeholders, in this context, could refer to both the public and private sector, civil society, and perhaps most importantly, the public at large. Although the term originated in the context of extractive industries, it is now applied to a much broader range of businesses including technologies like artificial intelligence (Candelon et al., 2022). As data becomes more commonly compared to exploitative practices like mining, it is only apt that we apply the concept of social licenses to the data ecosystem as well (Aitken et al., 2020).
Before exploring how to achieve social licenses for data reuse, it is important to understand the many factors that affect social licenses….(More)”.
Open data: The building block of 21st century (open) science
Paper by Corina Pascu and Jean-Claude Burgelman: “Given this irreversibility of data driven and reproducible science and the role machines will play in that, it is foreseeable that the production of scientific knowledge will be more like a constant flow of updated data driven outputs, rather than a unique publication/article of some sort. Indeed, the future of scholarly publishing will be more based on the publication of data/insights with the article as a narrative.
For open data to be valuable, reproducibility is a sine qua non (King2011; Piwowar, Vision and Whitlock2011) and—equally important as most of the societal grand challenges require several sciences to work together—essential for interdisciplinarity.
This trend correlates with the already ongoing observed epistemic shift in the rationale of science: from demonstrating the absolute truth via a unique narrative (article or publication), to the best possible understanding what at that moment is needed to move forward in the production of knowledge to address problem “X” (de Regt2017).
Science in the 21st century will be thus be more “liquid,” enabled by open science and data practices and supported or even co-produced by artificial intelligence (AI) tools and services, and thus a continuous flow of knowledge produced and used by (mainly) machines and people. In this paradigm, an article will be the “atomic” entity and often the least important output of the knowledge stream and scholarship production. Publishing will offer in the first place a platform where all parts of the knowledge stream will be made available as such via peer review.
The new frontier in open science as well as where most of future revenue will be made, will be via value added data services (such as mining, intelligence, and networking) for people and machines. The use of AI is on the rise in society, but also on all aspects of research and science: what can be put in an algorithm will be put; the machines and deep learning add factor “X.”
AI services for science 4 are already being made along the research process: data discovery and analysis and knowledge extraction out of research artefacts are accelerated with the use of AI. AI technologies also help to maximize the efficiency of the publishing process and make peer-review more objective5 (Table 1).
Table 1. Examples of AI services for science already being developed
Abbreviation: AI, artificial intelligence.
Source: Authors’ research based on public sources, 2021.
Ultimately, actionable knowledge and translation of its benefits to society will be handled by humans in the “machine era” for decades to come. But as computers are indispensable research assistants, we need to make what we publish understandable to them.
The availability of data that are “FAIR by design” and shared Application Programming Interfaces (APIs) will allow new ways of collaboration between scientists and machines to make the best use of research digital objects of any kind. The more findable, accessible, interoperable, and reusable (FAIR) data resources will become available, the more it will be possible to use AI to extract and analyze new valuable information. The main challenge is to master the interoperability and quality of research data…(More)”.
How can digital public technologies accelerate progress on the Sustainable Development Goals?
Report by George Ingram, John W. McArthur, and Priya Vora: “…There is no singular relationship between access to digital technologies and SDG outcomes. Country- and issue-specific assessments are essential. Sound approaches will frequently depend on the underlying physical infrastructure and economic systems. Rwanda, for instance, has made tremendous progress on SDG health indicators despite high rates of income poverty and internet poverty. This contrasts with Burkina Faso, which has lower income poverty and internet poverty but higher child mortality.
We draw from an OECD typology to identify three layers of a digital ecosystem: Physical infrastructure, platform infrastructure, and apps-level products. Physical and platform layers of digital infrastructure provide the rules, standards, and security guarantees so that local market innovators and governments can develop new ideas more rapidly to meet ever-changing circumstances. We emphasize five forms of DPT platform infrastructure that can play important roles in supporting SDG acceleration:
- Personal identification and registration infrastructure allows citizens and organizations to have equal access to basic rights and services;
- Payments infrastructure enables efficient resource transfer with low transaction costs;
- Knowledge infrastructure links educational resources and data sets in an open or permissioned way;
- Data exchange infrastructure enables interoperability of independent databases; and
- Mapping infrastructure intersects with data exchange platforms to empower geospatially enabled diagnostics and service delivery opportunities.
Each of these platform types can contribute directly or indirectly to a range of SDG outcomes. For example, a person’s ability to register their identity with public sector entities is fundamental to everything from a birth certificate (SDG target 16.9) to a land title (SDG 1.4), bank account (SDG 8.10), driver’s license, or government-sponsored social protection (SDG 1.3). It can also ensure access to publicly available basic services, such as access to public schools (SDG 4.1) and health clinics (SDG 3.8).
At least three levers can help “level the playing field” such that a wide array of service providers can use the physical and platform layers of digital infrastructure equally: (1) public ownership and governance; (2) public regulation; and (3) open code, standards, and protocols. In practice, DPTs are typically built and deployed through a mix of levers, enabling different public and private actors to extract benefits through unique pathways….(More)”.
We can’t create shared value without data. Here’s why
Article by Kriss Deiglmeier: “In 2011, I was co-teaching a course on Corporate Social Innovation at the Stanford Graduate School of Business, when our syllabus nearly went astray. A paper appeared in Harvard Business Review (HBR), titled “Creating Shared Value,” by Michael E. Porter and Mark R. Kramer. The students’ excitement was palpable: This could transform capitalism, enabling Adam Smith’s “invisible hand” to bend the arc of history toward not just efficiency and profit, but toward social impact…
History shows that the promise of shared value hasn’t exactly been realized. In the past decade, most indexes of inequality, health, and climate change have gotten worse, not better. The gap in wealth equality has widened – the combined worth of the top 1% in the United States increased from 29% of all wealth in 2011 to 32.3% in 2021 and the bottom 50% increased their share from 0.4% to 2.6% of overall wealth; everyone in between saw their share of wealth decline. The federal minimum wage has remained stagnant at $7.25 per hour while the US dollar has seen a cumulative price increase of 27.81%…
That said, data is by no means the only – or even primary – obstacle to achieving shared value, but the role of data is a key aspect that needs to change. In a shared value construct, data is used primarily for profit and not the societal benefit at the speed and scale required.
Unfortunately, the technology transformation has resulted in an emerging data divide. While data strategies have benefited the commercial sector, the public sector and nonprofits lag in education, tools, resources, and talent to use data in finding and scaling solutions. The result is the disparity between the expanding use of data to create commercial value, and the comparatively weak use of data to solve social and environmental challenges…
Data is part of our future and is being used by corporations to drive success, as they should. Bringing data into the shared value framework is about ensuring that other entities and organizations also have the access and tools to harness data for solving social and environmental challenges as well….
Business has the opportunity to help solve the data divide through a shared value framework by bringing talent, product and resources to bear beyond corporate boundaries to help solve our social and environmental challenges. To succeed, it’s essential to re-envision the shared value framework to ensure data is at the core to collectively solve these challenges for everyone. This will require a strong commitment to collaboration between business, government and NGOs – and it will undoubtedly require a dedication to increasing data literacy at all levels of education….(More)”.