Global collaboration on human migration launches digital hub

Curated on Posted on by Stefaan Verhulst

Press Release: “The International Organization for Migration (IOM) and the Joint Research Centre (JRC) of the European Commission joined forces with The Governance Lab (The GovLab) at the NYU Tandon School of Engineering to launch an online home for the Big Data for Migration (BD4M) Alliance, the first-ever global network dedicated to facilitating responsible data innovation and collaboration for informed decision making on migration and human mobility.

We live in a fast-moving world where a huge amount of data is being generated by the private sector but public-private data partnerships still remain limited. The BD4M, convened in 2018 by the European Commission’s Knowledge Centre on Migration and Demography (KCMD) and the IOM’s Global Migration Data Analysis Centre (GMDAC), seeks to foster more cooperation in this area by connecting stakeholders and leveraging non-traditional data sources to improve understanding.

The new BD4M web page, www.data4migration.org, hosted by the GovLab, serves as a hub for the Alliance’s activities. It aims to inform stakeholders about the BD4M members, its objectives, ongoing projects, upcoming events and opportunities for collaboration.

To facilitate access to knowledge about how data innovation has contributed to informing migration policy and programs, for example, the BD4M recently launched the Data Innovation Directory, which features examples of applications of new data sources and methodologies in the field of migration and human mobility.

The BD4M is open to members of international organizations, NGOs, the private sector, researchers and individual experts. In its partnership with The GovLab, the BD4M has helped identify a set of priority questions on migration that new data sources could contribute to answering. These questions were formulated by experts and validated through a public voting campaign as part of The 100 Questions Initiative….(More)”.

Digital contact tracing and surveillance during COVID-19

Curated on Posted on by Stefaan Verhulst

Report on General and Child-specific Ethical Issues by Gabrielle Berman, Karen Carter, Manuel García-Herranz and Vedran Sekara: “The last few years have seen a proliferation of means and approaches being used to collect sensitive or identifiable data on children. Technologies such as facial recognition and other biometrics, increased processing capacity for ‘big data’ analysis and data linkage, and the roll-out of mobile and internet services and access have substantially changed the nature of data collection, analysis, and use.

Real-time data are essential to support decision-makers in government, development and humanitarian agencies such as UNICEF to better understand the issues facing children, plan appropriate action, monitor progress and ensure that no one is left behind. But the collation and use of personally identifiable data may also pose significant risks to children’s rights.

UNICEF has undertaken substantial work to provide a foundation to understand and balance the potential benefits and risks to children of data collection. This work includes the Industry Toolkit on Children’s Online Privacy and Freedom of Expression and a partnership with GovLab on Responsible Data for Children (RD4C) – which promotes good practice principles and has developed practical tools to assist field offices, partners and governments to make responsible data management decisions.

Balancing the need to collect data to support good decision-making versus the need to protect children from harm created through the collection of the data has never been more challenging than in the context of the global COVID-19 pandemic. The response to the pandemic has seen an unprecedented rapid scaling up of technologies to support digital contact tracing and surveillance. The initial approach has included:

  • tracking using mobile phones and other digital devices (tablet computers, the Internet of Things, etc.)
  • surveillance to support movement restrictions, including through the use of location monitoring and facial recognition
  • a shift from in-person service provision and routine data collection to the use of remote or online platforms (including new processes for identity verification)
  • an increased focus on big data analysis and predictive modelling to fill data gaps…(More)”.

Dynamic Networks Improve Remote Decision-Making

Curated on Posted on by Stefaan Verhulst

Article by Abdullah Almaatouq and Alex “Sandy” Pentland: “The idea of collective intelligence is not new. Research has long shown that in a wide range of settings, groups of people working together outperform individuals toiling alone. But how do drastic shifts in circumstances, such as people working mostly at a distance during the COVID-19 pandemic, affect the quality of collective decision-making? After all, public health decisions can be a matter of life and death, and business decisions in crisis periods can have lasting effects on the economy.

During a crisis, it’s crucial to manage the flow of ideas deliberatively and strategically so that communication pathways and decision-making are optimized. Our recently published research shows that optimal communication networks can emerge from within an organization when decision makers interact dynamically and receive frequent performance feedback. The results have practical implications for effective decision-making in times of dramatic change….

Our experiments illustrate the importance of dynamically configuring network structures and enabling decision makers to obtain useful, recurring feedback. But how do you apply such findings to real-world decision-making, whether remote or face to face, when constrained by a worldwide pandemic? In such an environment, connections among individuals, teams, and networks of teams must be continually reorganized in response to shifting circumstances and challenges. No single network structure is optimal for every decision, a fact that is clear in a variety of organizational contexts.

Public sector. Consider the teams of advisers working with governments in creating guidelines to flatten the curve and help restart national economies. The teams are frequently reconfigured to leverage pertinent expertise and integrate data from many domains. They get timely feedback on how decisions affect daily realities (rates of infection, hospitalization, death) — and then adjust recommended public health protocols accordingly. Some team members move between levels, perhaps being part of a state-level team for a while, then federal, and then back to state. This flexibility ensures that people making big-picture decisions have input from those closer to the front lines.

Witness how Germany considered putting a brake on some of its reopening measures in response to a substantial, unexpected uptick in COVID-19 infections. Such time-sensitive decisions are not made effectively without a dynamic exchange of ideas and data. Decision makers must quickly adapt to facts reported by subject-area experts and regional officials who have the relevant information and analyses at a given moment….(More)“.

The Food Systems Dashboard

Curated on Posted on by Stefaan Verhulst

About: “The Food Systems Dashboard combines data from multiple sources to give users a complete view of food systems. Users can compare components of food systems across countries and regions. They can also identify and prioritize ways to sustainably improve diets and nutrition in their food systems.

Dashboards are useful tools that help users visualize and understand key information for complex systems. Users can track progress to see if policies or other interventions are working at a country or regional level

In recent years, the public health and nutrition communities have used dashboards to track the progress of health goals and interventions, including the Sustainable Development Goals. To our knowledge, this is the first dashboard that collects country-level data across all components of the food system.

The Dashboard contains over 150 indicators that measure components, drivers, and outcomes of food systems at the country level. As new indicators and data become available, the Dashboard will be updated. Most data used for the Dashboard is open source and available to download directly from the website. Data is pooled from FAO, Euromonitor International, World Bank, and other global and regional data sources….(More)”.

Policy Priority Inference

Curated on Posted on by Stefaan Verhulst

Turing Institute: “…Policy Priority Inference builds on a behavioural computational model, taking into account the learning process of public officials, coordination problems, incomplete information, and imperfect governmental monitoring mechanisms. The approach is a unique mix of economic theory, behavioural economics, network science and agent-based modelling. The data that feeds the model for a specific country (or a sub-national unit, such as a state) includes measures of the country’s DIs and how they have moved over the years, specified government policy goals in relation to DIs, the quality of government monitoring of expenditure, and the quality of the country’s rule of law.

From these data alone – and, crucially, with no specific information on government expenditure, which is rarely made available – the model can infer the transformative resources a country has historically allocated to transform its SDGs, and assess the importance of SDG interlinkages between DIs. Importantly, it can also reveal where previously hidden inefficiencies lie.

How does it work? The researchers modelled the socioeconomic mechanisms of the policy-making process using agent-computing simulation. They created a simulator featuring an agent called “Government”, which makes decisions about how to allocate public expenditure, and agents called “Bureaucrats”, each of which is essentially a policy-maker linked to a single DI. If a Bureaucrat is allocated some resource, they will use a portion of it to improve their DI, with the rest lost to some degree of inefficiency (in reality, inefficiencies range from simple corruption to poor quality policies and inefficient government departments).

How much resource a Bureaucrat puts towards moving their DI depends on that agent’s experience: if becoming inefficient pays off, they’ll keep doing it. During the process, Government monitors the Bureaucrats, occasionally punishing inefficient ones, who may then improve their behaviour. In the model, a Bureaucrat’s chances of getting caught is linked to the quality of a government’s real-world monitoring of expenditure, and the extent to which they are punished is reflected in the strength of that country’s rule of law.

Diagram of the Policy Priority Inference model
Using data on a country or state’s development indicators and its governance, Policy Priority Inference techniques can model how a government and its policy-makers allocate “transformational resources” to reach their sustainable development goals.

When the historical movements of a country’s DIs are reproduced through the internal workings of the model, the researchers have a powerful proxy for the real-world relationships between government activity, the movement of DIs, and the effects of the interlinkages between DIs, all of which are unique to that country. “Once we can match outcomes, we can discern something that’s going on in reality. But the fact that the method is matching the dynamics of real-world development indicators is just one of multiple ways that we validate our results,” Guerrero notes. This proxy can then be used to project which policy areas should be prioritised in future to best achieve the government’s specified development goals, including predictions of likely timescales.

What’s more, in combination with techniques from evolutionary computation, the model can identify DIs that are linked to large positive spillover effects. These DIs are dubbed “accelerators”. Targeting government resources at such development accelerators fosters not only more rapid results, but also more generalised development…(More)”.

An introduction to human rights for the mobile sector

Curated on Posted on by Stefaan Verhulst

Report by the GSMA: “Human rights risks are present throughout mobile operators’ value chains. These range from the treatment and conditions of people working in the supply chain to how operators’ own employees are treated and how the human rights of customers are respected online.

This summary provides a high-level introduction to the most salient human rights issues for mobile operators. The aim is to explain why the issues are relevant for operators and share initial practical guidance for companies beginning to focus and respond to human rights issues….(More)”.

Standards and Innovations in Information Technology and Communications

Curated on Posted on by Stefaan Verhulst

Book by Dina Šimunić and Ivica Pavić: “This book gives a thorough explanation of standardization, its processes, its life cycle, and its related organization on a national, regional and global level. The book provides readers with an insight in the interaction cycle between standardization organizations, government, industry, and consumers. The readers can gain a clear insight to standardization and innovation process, standards, and innovations life-cycle and the related organizations with all presented material in the field of information and communications technologies. The book introduces the reader to understand perpetual play of standards and innovation cycle, as the basis for the modern world.

  • Provides a thorough explanation of standardization and innovation in relation to communications engineering and information technology
  • Discusses the standardization and innovation processes and organizations on global, regional, and national levels
  • Interconnects standardization and innovation, showing the perpetual life-cycle that is the basis of technology progress…(More)”.

Why open science is critical to combatting COVID-19

Curated on Posted on by Stefaan Verhulst

Article by the OECD: “…In January 2020, 117 organisations – including journals, funding bodies, and centres for disease prevention – signed a statement titled “Sharing research data and findings relevant to the novel coronavirus outbreak”, committing to provide immediate open access for peer-reviewed publications at least for the duration of the outbreak, to make research findings available via preprint servers, and to share results immediately with the World Health Organization (WHO). This was followed in March by the Public Health Emergency COVID-19 Initiative, launched by 12 countries1 at the level of chief science advisors or equivalent, calling for open access to publications and machine-readable access to data related to COVID-19, which resulted in an even stronger commitment by publishers.

The Open COVID Pledge was launched in April 2020 by an international coalition of scientists, lawyers, and technology companies, and calls on authors to make all intellectual property (IP) under their control available, free of charge, and without encumbrances to help end the COVID-19 pandemic, and reduce the impact of the disease….

Remaining challenges

While clinical, epidemiological and laboratory data about COVID-19 is widely available, including genomic sequencing of the pathogen, a number of challenges remain:

  • All data is not sufficiently findable, accessible, interoperable and reusable (FAIR), or not yet FAIR data.
  • Sources of data tend to be dispersed, even though many pooling initiatives are under way, curation needs to be operated “on the fly”.
  • Providing access to personal health record sharing needs to be readily accessible, pending the patient’s consent. Legislation aimed at fostering interoperability and avoiding information blocking are yet to be passed in many OECD countries. Access across borders is even more difficult under current data protection frameworks in most OECD countries.
  • In order to achieve the dual objectives of respecting privacy while ensuring access to machine readable, interoperable and reusable clinical data, the Virus Outbreak Data Network (VODAN) proposes to create FAIR data repositories which could be used by incoming algorithms (virtual machines) to ask specific research questions.
  • In addition, many issues arise around the interpretation of data – this can be illustrated by the widely followed epidemiological statistics. Typically, the statistics concern “confirmed cases”, “deaths” and “recoveries”. Each of these items seem to be treated differently in different countries, and are sometimes subject to methodological changes within the same country.
  • Specific standards for COVID-19 data therefore need to be established, and this is one of the priorities of the UK COVID-19 Strategy. A working group within Research Data Alliance has been set up to propose such standards at an international level.
  • In some cases it could be inferred that the transparency of the statistics may have guided governments to restrict testing in order to limit the number of “confirmed cases” and avoid the rapid rise of numbers. Lower testing rates can in turn reduce the efficiency of quarantine measures, lowering the overall efficiency of combating the disease….(More)”.

Measuring the predictability of life outcomes with a scientific mass collaboration

Curated on Posted on by Stefaan Verhulst

Paper by Matthew J. Salganik et al: “Hundreds of researchers attempted to predict six life outcomes, such as a child’s grade point average and whether a family would be evicted from their home. These researchers used machine-learning methods optimized for prediction, and they drew on a vast dataset that was painstakingly collected by social scientists over 15 y. However, no one made very accurate predictions. For policymakers considering using predictive models in settings such as criminal justice and child-protective services, these results raise a number of concerns. Additionally, researchers must reconcile the idea that they understand life trajectories with the fact that none of the predictions were very accurate….(More)”.

How Humanitarian Blockchain Can Deliver Fair Labor to Global Supply Chains

Curated on Posted on by Stefaan Verhulst

Paper by  Ashley Mehra and John G. Dale: “Blockchain technology in global supply chains has proven most useful as a tool for storing and keeping records of information or facilitating payments with increased efficiency. The use of blockchain to improve supply chains for humanitarian projects has mushroomed over the last five years; this increased popularity is in large part due to the potential for transparency and security that the design of the technology proposes to offer. Yet, we want to ask an important but largely unexplored question in the academic literature about the human rights of the workers who produce these “humanitarian blockchain” solutions: “How can blockchain help eliminate extensive labor exploitation issues embedded within our global supply chains?”

To begin to answer this question, we suggest that proposed humanitarian blockchain solutions must (1) re-purpose the technical affordances of blockchain to address relations of power that, sometimes unwittingly, exploit and prevent workers from collectively exercising their voice; (2) include legally or socially enforceable mechanisms that enable workers to meaningfully voice their knowledge of working conditions without fear of retaliation; and (3) re-frame our current understanding of human rights issues in the context of supply chains to include the labor exploitation within supply chains that produce and sustain the blockchain itself….(More)”.