Exploring a new governance agenda: What are the questions that matter?

Curated on Posted on by Stefaan Verhulst

Article by Nicola Nixon, Stefaan Verhulst, Imran Matin & Philips J. Vermonte: “…Late last year, we – the Governance Lab at NYU, the CSIS Indonesia, the BRAC Institute of Governance and Development, Bangladesh and The Asia Foundation – joined forces across New York, Jakarta, Dhaka, Hanoi, and San Francisco to launch the 100 Governance Questions Initiative. This is the latest iteration of the GovLab’s broader initiative to map questions across several domains.

We live in an era marked by an unprecedented amount of data. Anyone who uses a mobile phone or accesses the internet is generating vast streams of information. Covid-19 has only intensified this phenomenon. 

Although this data contains tremendous potential for positive social transformation, much of that potential goes unfulfilled. In the development context, one chief problem is that data initiatives are often driven by supply (i.e., what data or data solutions are available?) rather than demand (what problems actually need solutions?). Too many projects begin with the database, the app, the dashboard–beholden to the seduction of technology– and now, many parts of the developing world are graveyards of tech pilots. As is well established in development theory but not yet fully in practice, solution-driven governance interventions are destined to fail.

The 100 Questions Initiative, pioneered by the GovLab, seeks to overcome the chasm between supply and demand. It begins not by searching for what data is available, but by asking important questions about the biggest challenges societies and countries face, and then seeking more targeted and relevant data solutions. In doing this, it narrows the gap between policy makers and constituents, providing opportunities for improved evidence-based policy and community engagement in developing countries. As part of this initiative, we seek to define the ten most important questions across several domains, including Migration, Gender, Employment, the Future of Work, and—now–Governance.

On this occasion, we invited over 100 experts and practitioners in governance and data science –whom we call “bilinguals”– from various organizations, companies, and government agencies to identify what they see as the most pressing governance questions in their respective domains. Over 100 bilinguals were encouraged to prioritize potential impact, novelty, and feasibility in their questioning — moving toward a roadmap for data-driven action and collaboration that is both actionable and ambitious.   

By June, the bilinguals had articulated 170 governance-related questions. Over the next couple of months, these were sorted, discussed and refined during two rounds of collaboration with the bilinguals; first to narrow down to the top 40 and then to the top 10. Bilinguals were asked what, to them, are the most significant governance questions we must answer with data today? The result is the following 10 questions:…(More)” ( Public Voting Platform)”.

Climate change versus children: How a UNICEF data collaborative gave birth to a risk index

Curated on Posted on by Stefaan Verhulst

Jess Middleton at DataIQ: “Almost a billion children face climate-related disasters in their lifetime, according to UNICEF’s new Children’s Climate Risk Index (CCRI).

The CCRI is the first analysis of climate risk specifically from a child’s perspective. It reveals that children in Central African Republic, Chad and Nigeria are at the highest risk from climate and environmental shocks based on their access to essential services….

Young climate activists including Greta Thunberg contributed a foreword to the report that introduced the index; and the project has added another layer of pressure on governments failing to act on climate change in the run-up to the 2021 United Nations Climate Change Conference – set to be held in Glasgow in November.

While these statistics make for grim reading, the collective effort undertaken to create the Index is evidence of the power of data as a tool for advocacy and the role that data collaboratives can play in shaping positive change.

The CCRI is underpinned by data that was sourced, collated and analysed by the Data for Children Collaborative with UNICEF, a partnership between UNICEF, the Scottish Government and University of Edinburgh hosted by The Data Lab.

The collaboration brings together practitioners from diverse backgrounds to provide data-driven solutions to issues faced by children around the world.

For work on the CCRI, the collaborative sought data, skills and expertise from academia (Universities of Southampton, Edinburgh, Stirling, Highlands and Islands) as well as the public and private sectors (ONS-FCDO Data Science Hub, Scottish Alliance for Geoscience, Environment & Society).

This variety of expertise provided the knowledge required to build the two main pillars of input for the CCRI: socioeconomic and climate science data.

Socioeconomic experts sourced data and provided analytical expertise in the context of child vulnerability, social statistics, biophysical processes and statistics, child welfare and child poverty.

Climate experts focused on factors such as water scarcity, flood exposure, coastal flood risk, pollution and exposure to vector borne disease.

The success of the project hinged on the effective collaboration between distinct areas of expertise to deliver on UNICEF’s problem statement.

The director of the Data for Children Collaborative with UNICEF, Alex Hutchison, spoke with DataIQ about the success of the project, the challenges the team faced, and the benefits of working as part of a diverse collective….(More). (Report)”

Using Satellite Imagery and Machine Learning to Estimate the Livelihood Impact of Electricity Access

Curated on Posted on by Stefaan Verhulst

Paper by Nathan Ratledge et al: “In many regions of the world, sparse data on key economic outcomes inhibits the development, targeting, and evaluation of public policy. We demonstrate how advancements in satellite imagery and machine learning can help ameliorate these data and inference challenges. In the context of an expansion of the electrical grid across Uganda, we show how a combination of satellite imagery and computer vision can be used to develop local-level livelihood measurements appropriate for inferring the causal impact of electricity access on livelihoods. We then show how ML-based inference techniques deliver more reliable estimates of the causal impact of electrification than traditional alternatives when applied to these data. We estimate that grid access improves village-level asset wealth in rural Uganda by 0.17 standard deviations, more than doubling the growth rate over our study period relative to untreated areas. Our results provide country-scale evidence on the impact of a key infrastructure investment, and provide a low-cost, generalizable approach to future policy evaluation in data sparse environments….(More)”.

Can national statistical offices shape the data revolution?

Curated on Posted on by Stefaan Verhulst

Article by Juan Daniel Oviedo, Katharina Fenz, François Fonteneau, and Simon Riedl: “In recent years, breakthrough technologies in artificial intelligence (AI) and the use of satellite imagery made it possible to disrupt the way we collect, process, and analyze data. Facilitated by the intersection of new statistical techniques and the availability of (big) data, it is now possible to create hypergranular estimates.

National statistical offices (NSOs) could be at the forefront of this change. Conventional tasks of statistical offices, such as the coordination of household surveys and censuses, will remain at the core of their work. However, just like AI can enhance the capabilities of doctors, it also has the potential to make statistical offices better, faster, and eventually cheaper.

Still, many countries struggle to make this happen. In a COVID-19 world marked by constrained financial and statistical capacities, making innovation work for statistical offices is of prime importance to create better lives for all…

In the case of Colombia, this novel method facilitated a scale-up from existing poverty estimates that contained 1,123 data points to 78,000 data points, which represents a 70-fold increase. This results in much more granular estimates highlighting Colombia’s heterogeneity between and within municipalities (see Figure 1).

Figure 1. Poverty shares (%) Colombia, in 2018

Figure 1. Poverty shares (%) Colombia, in 2018

Traditional methods don´t allow for cost-efficient hypergranular estimations but serve as a reference point, due to their ground-truthing capacity. Hence, we have combined existing data with novel AI techniques, to go down to granular estimates of up to 4×4 kilometers. In particular, we have trained an algorithm to connect daytime and nighttime satellite images….(More)”.

The Innovation Project: Can advanced data science methods be a game-change for data sharing?

Curated on Posted on by Stefaan Verhulst

Report by JIPS (Joint Internal Displacement Profiling Service): “Much has changed in the humanitarian data landscape in the last decade and not primarily with the arrival of big data and artificial intelligence. Mostly, the changes are due to increased capacity and resources to collect more data quicker, leading to the professionalisation of information management as a domain of work. Larger amounts of data are becoming available in a more predictable way. We believe that as the field has progressed in filling critical data gaps, the problem is not the availability of data, but the curation and sharing of that data between actors as well as the use of that data to its full potential.

In 2018, JIPS embarked on an innovation journey to explore the potential of state-of-the-art technologies to incentivise data sharing and collaboration. This report covers the first phase of the innovation project and launches a series of articles in which we will share more about the innovation journey itself, discuss safe data sharing and collaboration, and look at the prototype we developed – made possible by the UNHCR Innovation Fund.

We argue that by making data and insights safe and secure to share between stakeholders, it will allow for a more efficient use of available data, reduce the resources needed to collect new data, strengthen collaboration and foster a culture of trust in the evidence-informed protection of people in displacement and crises.

The paper first defines the problem and outlines the processes through which data is currently shared among the humanitarian community. It explores questions such as: what are the existing data sharing methods and technologies? Which ones constitute a feasible option for humanitarian and development organisations? How can different actors share and collaborate on datasets without impairing confidentiality and exposing them to disclosure threats?…(More)”.

Building a Responsible Open Data Ecosystem: Mobility Data & COVID-19

Curated on Posted on by Stefaan Verhulst

Blog by Anna Livaccari: “Over the last year and a half, COVID-19 has changed the way people move, work, shop, and live. The pandemic has necessitated new data-sharing initiatives to understand new patterns of movement, analyze the spread of COVID-19, and inform research and decision-making. Earlier this year, Cuebiq collaborated with the Open Data Institute (ODI) and NYU’s The GovLab to explore the efficacy of these new initiatives. 

The ODI is a non-profit organization that brings together commercial and non-commercial organizations and governments to address global issues as well as advise on how data can be used for positive social good. As part of a larger project titled “COVID-19: Building an open and trustworthy data ecosystem,” the ODI published a new report with Cuebiq and The GovLab, an action research center at NYU’s Tandon School of Engineering that has pioneered the concept of data collaboratives and runs the data stewards network among other initiatives to advance data-driven decision making in the public interest. This report, “The Use of Mobility Data for Responding to the COVID-19 Pandemic,” specifically addresses key enablers and obstacles to the successful sharing of mobility data between public and private organizations during the pandemic….

Since early 2020, researchers and policy makers have been eager to understand the impact of COVID-19. With the help of mobility data, organizations from different sectors were able to answer some of the most pressing questions regarding the pandemic: questions about policy decisions, mass-communication strategies, and overall socioeconomic impact. Mobility data can be applied to specific use cases and can help answer complex questions, a fact that The GovLab discusses in its short-form mobility data brief. Understanding exactly how organizations employ mobility data can also improve how institutions operate post-pandemic and make data collaboration as a whole more responsible, sustainable, and systemic.

Cuebiq and the GovLab identified 51 projects where mobility data was used for pandemic response, and then selected five case studies to analyze further. The report defines mobility data, the ethics surrounding it, and the lessons learned for the future….(More)”.

The Mobility Data Sharing Assessment

Curated on Posted on by Stefaan Verhulst

New Tool from the Mobility Data Collaborative (MDC): “…released a set of resources to support transparent and accountable decision making about how and when to share mobility data between organizations. …The Mobility Data Sharing Assessment (MDSA) is a practical and customizable assessment that provides operational guidance to support an organization’s existing processes when sharing or receiving mobility data. It consists of a collection of resources:

  • 1. A Tool that provides a practical, customizable and open-source assessment for organizations to conduct a self-assessment.
  • 2. An Operator’s Manual that provides detailed instructions, guidance and additional resources to assist organizations as they complete the tool.
  • 3. An Infographic that provides a visual overview of the MDSA process.

“We were excited to work with the MDC to create a practical set of resources to support mobility data sharing between organizations,” said Chelsey Colbert, policy counsel at FPF. “Through collaboration, we designed version one of a technology-neutral tool, which is consistent and interoperable with leading industry frameworks. The MDSA was designed to be a flexible and scalable approach that enables mobility data sharing initiatives by encouraging organizations of all sizes to assess the legal, privacy, and ethical considerations.”

New mobility options, such as shared cars and e-scooters, have rapidly emerged in cities over the past decade. Data generated by these mobility services offers an exciting opportunity to provide valuable and timely insight to effectively develop transportation policy and infrastructure. As the world becomes more data-driven, tools like the MDSA help remove barriers to safe data sharing without compromising consumer trust….(More)”.

Data in Crisis — Rethinking Disaster Preparedness in the United States

Curated on Posted on by Stefaan Verhulst

Paper by Satchit Balsari, Mathew V. Kiang, and Caroline O. Buckee: “…In recent years, large-scale streams of digital data on medical needs, population vulnerabilities, physical and medical infrastructure, human mobility, and environmental conditions have become available in near-real time. Sophisticated analytic methods for combining them meaningfully are being developed and are rapidly evolving. However, the translation of these data and methods into improved disaster response faces substantial challenges. The data exist but are not readily accessible to hospitals and response agencies. The analytic pipelines to rapidly translate them into policy-relevant insights are lacking, and there is no clear designation of responsibility or mandate to integrate them into disaster-mitigation or disaster-response strategies. Building these integrated translational pipelines that use data rapidly and effectively to address the health effects of natural disasters will require substantial investments, and these investments will, in turn, rely on clear evidence of which approaches actually improve outcomes. Public health institutions face some ongoing barriers to achieving this goal, but promising solutions are available….(More)”

WHO, Germany open Hub for Pandemic and Epidemic Intelligence in Berlin

Curated on Posted on by Stefaan Verhulst

Press Release: “To better prepare and protect the world from global disease threats, H.E. German Federal Chancellor Dr Angela Merkel and Dr Tedros Adhanom Ghebreyesus, World Health Organization Director-General, will today inaugurate the new WHO Hub for Pandemic and Epidemic Intelligence, based in Berlin. 

“The world needs to be able to detect new events with pandemic potential and to monitor disease control measures on a real-time basis to create effective pandemic and epidemic risk management,” said Dr Tedros. “This Hub will be key to that effort, leveraging innovations in data science for public health surveillance and response, and creating systems whereby we can share and expand expertise in this area globally.” 

The WHO Hub, which is receiving an initial investment of US$ 100 million from the Federal Republic of Germany, will harness broad and diverse partnerships across many professional disciplines, and the latest technology, to link the data, tools and communities of practice so that actionable data and intelligence are shared for the common good.

The  WHO Hub is part of WHO’s Health Emergencies Programme and will be a new collaboration of countries and partners worldwide, driving innovations to increase availability of key data; develop state of the art analytic tools and predictive models for risk analysis; and link communities of practice around the world. Critically, the WHO Hub will support the work of public health experts and policy-makers in all countries with the tools needed to forecast, detect and assess epidemic and pandemic risks so they can take rapid decisions to prevent and respond to future public health emergencies.

“Despite decades of investment, COVID-19 has revealed the great gaps that exist in the world’s ability to forecast, detect, assess and respond to outbreaks that threaten people worldwide,” said Dr Michael Ryan, Executive Director of WHO’s Health Emergency Programme. “The WHO Hub for Pandemic and Epidemic Intelligence is designed to develop the data access, analytic tools and communities of practice to fill these very gaps, promote collaboration and sharing, and protect the world from such crises in the future.” 

The Hub will work to:

  • Enhance methods for access to multiple data sources vital to generating signals and insights on disease emergence, evolution and impact;
  • Develop state of the art tools to process, analyze and model data for detection, assessment and response;
  • Provide WHO, our Member States, and partners with these tools to underpin better, faster decisions on how to address outbreak signals and events; and
  • Connect and catalyze institutions and networks developing disease outbreak solutions for the present and future.

Dr Chikwe Ihekweazu, currently Director-General of the Nigeria Centre for Disease Control, has been appointed to lead the WHO Hub….(More)” 

The Open-Source Movement Comes to Medical Datasets

Curated on Posted on by Stefaan Verhulst

Blog by Edmund L. Andrews: “In a move to democratize research on artificial intelligence and medicine, Stanford’s Center for Artificial Intelligence in Medicine and Imaging (AIMI) is dramatically expanding what is already the world’s largest free repository of AI-ready annotated medical imaging datasets.

Artificial intelligence has become an increasingly pervasive tool for interpreting medical images, from detecting tumors in mammograms and brain scans to analyzing ultrasound videos of a person’s pumping heart.

Many AI-powered devices now rival the accuracy of human doctors. Beyond simply spotting a likely tumor or bone fracture, some systems predict the course of a patient’s illness and make recommendations.

But AI tools have to be trained on expensive datasets of images that have been meticulously annotated by human experts. Because those datasets can cost millions of dollars to acquire or create, much of the research is being funded by big corporations that don’t necessarily share their data with the public.

“What drives this technology, whether you’re a surgeon or an obstetrician, is data,” says Matthew Lungren, co-director of AIMI and an assistant professor of radiology at Stanford. “We want to double down on the idea that medical data is a public good, and that it should be open to the talents of researchers anywhere in the world.”

Launched two years ago, AIMI has already acquired annotated datasets for more than 1 million images, many of them from the Stanford University Medical Center. Researchers can download those datasets at no cost and use them to train AI models that recommend certain kinds of action.

Now, AIMI has teamed up with Microsoft’s AI for Health program to launch a new platform that will be more automated, accessible, and visible. It will be capable of hosting and organizing scores of additional images from institutions around the world. Part of the idea is to create an open and global repository. The platform will also provide a hub for sharing research, making it easier to refine different models and identify differences between population groups. The platform can even offer cloud-based computing power so researchers don’t have to worry about building local resource intensive clinical machine-learning infrastructure….(More)”.