Building the World We Deserve: A New Framework for Infrastructure

Curated on Posted on by Stefaan Verhulst

Introductory letter to a new whitepaper published by Siegel Family Endowment that outlines a new framework for understanding and funding infrastructure: “This story begins, as many set in New York City do, with the subway. As transportation enthusiasts, we’re fascinated by trains, especially the remarkable system that runs above and below the city’s streets. It was the discovery of this shared passion for understanding how our subway system works that got us talking about infrastructure a few years ago.

Infrastructure, in the most traditional sense, brings to mind physical constructions: city streets, power lines, the pipes that carry water into your home. But what about all the other things that make society function? Having seen the decline in investment in the country’s physical infrastructure, and aware of the many ways the digital world is upending our definition of the term, we began exploring how Siegel Family Endowment could play a role in the future of infrastructure.

Over the past two years of research and conversations with partners across the field, we’ve realized that our nation’s infrastructure is due for a reset. Hearing the term should evoke a different image: an interconnected web of assets, seen and unseen, that make up the foundation upon which the complicated machinery of modern society operates. It’s inherently multidimensional.

In 2020, the United States has reckoned with a health pandemic and a watershed moment in the fight for racial equity. These challenges highlight how relevant it is to reconsider what society deems the most critical, foundational assets for its citizens—and to ensure they have access to those assets.

Funding infrastructure is often considered the responsibility of government agencies. Yet many of our peers in philanthropy have made important investments in the field. These include working with local governments to fund research, promote novel forms of public-private partnership, and, ultimately, better serve citizens. And if infrastructure is viewed through the broader lens we argue for in this paper, it becomes clear just how much philanthropy, the nonprofit sector, and private entities are investing in our digital and social ecosystems.

We believe that we can do more—and better—if we commit as a country to adopting some of the principles outlined in this paper. However, we also consider this the beginning of a conversation. The time for us to think bigger and bolder about infrastructure is here. Our challenge now is to design it so that more people may thrive….(More)”.

Lessons learned from AI ethics principles for future actions

Curated on Posted on by Stefaan Verhulst

Paper by Merve Hickok: “As the use of artificial intelligence (AI) systems became significantly more prevalent in recent years, the concerns on how these systems collect, use and process big data also increased. To address these concerns and advocate for ethical and responsible development and implementation of AI, non-governmental organizations (NGOs), research centers, private companies, and governmental agencies published more than 100 AI ethics principles and guidelines. This first wave was followed by a series of suggested frameworks, tools, and checklists that attempt a technical fix to issues brought up in the high-level principles. Principles are important to create a common understanding for priorities and are the groundwork for future governance and opportunities for innovation. However, a review of these documents based on their country of origin and funding entities shows that private companies from US-West axis dominate the conversation. Several cases surfaced in the meantime which demonstrate biased algorithms and their impact on individuals and society. The field of AI ethics is urgently calling for tangible action to move from high-level abstractions and conceptual arguments towards applying ethics in practice and creating accountability mechanisms. However, lessons must be learned from the shortcomings of AI ethics principles to ensure the future investments, collaborations, standards, codes or legislation reflect the diversity of voices and incorporate the experiences of those who are already impacted by the biased algorithms….(More)”.

How to fix the GDPR’s frustration of global biomedical research

Curated on Posted on by Stefaan Verhulst

Jasper Bovenberg, David Peloquin, Barbara Bierer, Mark Barnes, and Bartha Maria Knoppers at Science: “Since the advent of the European Union (EU) General Data Protection Regulation (GDPR) in 2018, the biomedical research community has struggled to share data with colleagues and consortia outside the EU, as the GDPR limits international transfers of personal data. A July 2020 ruling of the Court of Justice of the European Union (CJEU) reinforced obstacles to sharing, and even data transfer to enable essential research into coronavirus disease 2019 (COVID-19) has been restricted in a recent Guidance of the European Data Protection Board (EDPB). We acknowledge the valid concerns that gave rise to the GDPR, but we are concerned that the GDPR’s limitations on data transfers will hamper science globally in general and biomedical science in particular (see the text box) (1)—even though one stated objective of the GDPR is that processing of personal data should serve humankind, and even though the GDPR explicitly acknowledges that the right to the protection of personal data is not absolute and must be considered in relation to its function in society and be balanced against other fundamental rights. We examine whether there is room under the GDPR for EU biomedical researchers to share data from the EU with the rest of the world to facilitate biomedical research. We then propose solutions for consideration by either the EU legislature, the EU Commission, or the EDPB in its planned Guidance on the processing of health data for scientific research. Finally, we urge the EDPB to revisit its recent Guidance on COVID-19 research….(More)“.

Situating Open Data: Global Trends in Local Contexts

Curated on Posted on by Stefaan Verhulst

Open Access Book edited by Danny Lämmerhirt, Ana Brandusescu, Natalia Domagala & Patrick Enaholo: “Open data and its effects on society are always woven into infrastructural legacies, social relations, and the political economy. This raises questions about how our understanding and engagement with open data shifts when we focus on its situated use. 

To shed a light on these questions, Situating Open Data provides several empirical accounts of open data practices, the local implementation of global initiatives, and the development of new open data ecosystems. Drawing on case studies in different countries and contexts, the chapters demonstrate the practices and actors involved in open government data initiatives unfolding within different socio-political settings. 

The book proposes three recommendations for researchers, policy-makers and practitioners. First, beyond upskilling through ‘data literacy’ programmes, open data initiatives should be specified through the kinds of data practices and effects they generate. Second, global visions of open data implementation require more studies of the resonances and tensions created in localised initiatives. And third, research into open data ecosystems requires more attention to the histories and legacies of information infrastructures and how these shape who benefits from open data flows. 

As such, this volume departs from the framing of data as a resource to be deployed. Instead, it proposes a prism of different data practices in different contexts through which to study the social relations, capacities, infrastructural histories and power structures affecting open data initiatives. It is hoped that the contributions collected in Situating Open Data will spark critical reflection about the way open data is locally practiced and implemented. The contributions should be of interest to open data researchers, advocates, and those in or advising government administrations designing and rolling out effective open data initiatives….(More)”.

Science and Scientists Held in High Esteem Across Global Publics

Curated on Posted on by Stefaan Verhulst

Pew Research: “As publics around the world look to scientists and the research and development process to bring new treatments and preventive strategies for the novel coronavirus, a new international survey finds scientists and their research are widely viewed in a positive light across global publics, and large majorities believe government investments in scientific research yield benefits for society.

Chart shows most value government investment in scientific research, being a world leader in science

Still, the wide-ranging survey, conducted before the COVID-19 outbreak reached pandemic proportions, reveals ambivalence about certain scientific developments – in areas such as artificial intelligence and genetically modified foods – often exists alongside high trust for scientists generally and positive views in other areas such as space exploration….

Scientists as a group are highly regarded, compared with other prominent groups and institutions in society. In all publics, majorities have at least some trust in scientists to do what is right. A median of 36% have “a lot” of trust in scientists, the same share who say this about the military, and much higher than the shares who say this about business leaders, the national government and the news media.

Still, an appreciation for practical experience, more so than expertise, in general, runs deep across publics. A median of 66% say it’s better to rely on people with practical experience to solve pressing problems, while a median of 28% say it’s better to rely on people who are considered experts about the problems, even if they don’t have much practical experience….(More)”.

The Wisdom of the Crowd: Promoting Media Development through Deliberative Initiatives

Curated on Posted on by Stefaan Verhulst

Report by Craig Matasick: “…innovative new set of citizen engagement practices—collectively known as deliberative democracy—offers important lessons that, when applied to the media development efforts, can help improve media assistance efforts and strengthen independent media environments around the world. At a time when disinformation runs rampant, it is more important than ever to strengthen public demand for credible information, reduce political polarization, and prevent media capture. Deliberative democracy approaches can help tackle these issues by expanding the number and diversity of voices that participate in policymaking, thereby fostering greater collective action and enhancing public support for media reform efforts.

Through a series of five illustrative case studies, the report demonstrates how deliberative democracy practices can be employed in both media development and democracy assistance efforts, particularly in the Global South. Such initiatives produce recommendations that take into account a plurality of voices while building trust between citizens and decision-makers by demonstrating to participants that their issues will be heard and addressed. Ultimately, this process can enable media development funders and practitioners to identify priorities and design locally relevant projects that have a higher likelihood for long-term impact.

– Deliberative democracy approaches, which are characterized by representative participation and moderated deliberation, provide a framework to generate demand-driven media development interventions while at the same time building greater public support for media reform efforts.

– Deliberative democracy initiatives foster collaboration across different segments of society, building trust in democratic institutions, combatting polarization, and avoiding elite capture.

– When employed by news organizations, deliberative approaches provide a better understanding of the issues their audiences care most about and uncover new problems affecting citizens that might not otherwise have come to light….(More)”.

Private Sector Data for Humanitarian Response: Closing the Gaps

Curated on Posted on by Stefaan Verhulst

Jos Berens at Bloomberg New Economy Forum: “…Despite these and other examples, data sharing between the private sector and humanitarian agencies is still limited. Out of 281 contributing organizations on HDX, only a handful come from the private sector. 

So why don’t we see more use of private sector data in humanitarian response? One obvious set of challenges concerns privacy, data protection and ethics. Companies and their customers are often wary of data being used in ways not related to the original purpose of data collection. Such concerns are understandable, especially given the potential legal and reputational consequences of personal data breaches and leaks.

Figuring out how to use this type of sensitive data in an already volatile setting seems problematic, and it is — negotiations between public and private partners in the middle of a crisis often get hung up on a lack of mutual understanding. Data sharing partnerships negotiated during emergencies often fail to mature beyond the design phase. This dynamic creates a loop of inaction due to a lack of urgency in between crises, followed by slow and halfway efforts when action is needed most.

To ensure that private sector data is accessible in an emergency, humanitarian organizations and private sector companies need to work together to build partnerships before a crisis. They can do this by taking the following actions: 

  • Invest in relationships and build trust. Both humanitarian organizations and private sector organizations should designate focal points who can quickly identify potentially useful data during a humanitarian emergency. A data stewards network which identifies and connects data responsibility leaders across organizations, as proposed by the NYU Govlab, is a great example of how such relations could look. Efforts to build trust with the general public regarding private sector data use for humanitarian response should also be strengthened, primarily through transparency about the means and purpose of such collaborations. This is particularly important in the context of COVID-19, as noted in the UN Comprehensive Response to COVID-19 and the World Economic Forum’s ‘Great Reset’ initiative…(More)”.

An Open-Source Tool to Accelerate Scientific Knowledge Discovery

Curated on Posted on by Stefaan Verhulst

Mozilla: “Timely and open access to novel outputs is key to scientific research. It allows scientists to reproduce, test, and build on one another’s work — and ultimately unlock progress.

The most recent example of this is the research into COVID-19. Much of the work was published in open access journals, swiftly reviewed and ultimately improving our understanding of how to slow the spread and treat the disease. Although this rapid increase in scientific publications is evident in other domains too, we might not be reaping the benefits. The tools to parse and combine this newly created knowledge have roughly remained the same for years.

Today, Mozilla Fellow Kostas Stathoulopoulos is launching Orion — an open-source tool to illuminate the science behind the science and accelerate knowledge discovery in the life sciences. Orion enables users to monitor progress in science, visually explore the scientific landscape, and search for relevant publications.

Orion

Orion collects, enriches and analyses scientific publications in the life sciences from Microsoft Academic Graph.

Users can leverage Orion’s views to interact with the data. The Exploration view shows all of the academic publications in a three-dimensional visualization. Every particle is a paper and the distance between them signifies their semantic similarity; the closer two particles are, the more semantically similar. The Metrics view visualizes indicators of scientific progress and how they have changed over time for countries and thematic topics. The Search view enables the users to search for publications by submitting either a keyword or a longer query, for example, a sentence or a paragraph of a blog they read online….(More)”.

Why Modeling the Spread of COVID-19 Is So Damn Hard


Curated on Posted on by Stefaan Verhulst

Matthew Hutson at IEEE Spectrum: “…Researchers say they’ve learned a lot of lessons modeling this pandemic, lessons that will carry over to the next.

The first set of lessons is all about data. Garbage in, garbage out, they say. Jarad Niemi, an associate professor of statistics at Iowa State University who helps run the forecast hub used by the CDC, says it’s not clear what we should be predicting. Infections, deaths, and hospitalization numbers each have problems, which affect their usefulness not only as inputs for the model but also as outputs. It’s hard to know the true number of infections when not everyone is tested. Deaths are easier to count, but they lag weeks behind infections. Hospitalization numbers have immense practical importance for planning, but not all hospitals release those figures. How useful is it to predict those numbers if you never have the true numbers for comparison? What we need, he said, is systematized random testing of the population, to provide clear statistics of both the number of people currently infected and the number of people who have antibodies against the virus, indicating recovery. Prakash, of Georgia Tech, says governments should collect and release data quickly in centralized locations. He also advocates for central repositories of policy decisions, so modelers can quickly see which areas are implementing which distancing measures.

Researchers also talked about the need for a diversity of models. At the most basic level, averaging an ensemble of forecasts improves reliability. More important, each type of model has its own uses—and pitfalls. An SEIR model is a relatively simple tool for making long-term forecasts, but the devil is in the details of its parameters: How do you set those to match real-world conditions now and into the future? Get them wrong and the model can head off into fantasyland. Data-driven models can make accurate short-term forecasts, and machine learning may be good for predicting complicated factors. But will the inscrutable computations of, for instance, a neural network remain reliable when conditions change? Agent-based models look ideal for simulating possible interventions to guide policy, but they’re a lot of work to build and tricky to calibrate.

Finally, researchers emphasize the need for agility. Niemi of Iowa State says software packages have made it easier to build models quickly, and the code-sharing site GitHub lets people share and compare their models. COVID-19 is giving modelers a chance to try out all their newest tools, says Meyers, of the University of Texas. “The pace of innovation, the pace of development, is unlike ever before,” she says. “There are new statistical methods, new kinds of data, new model structures.”…(More)”.

Public Sector Tech: New tools for the new normal

Curated on Posted on by Stefaan Verhulst

Special issue by ZDNet exploring “how new technologies like AI, cloud, drones, and 5G are helping government agencies, public organizations, and private companies respond to the events of today and tomorrow…: