Lessons learned from telco data informing COVID-19 responses: toward an early warning system for future pandemics?

Curated on Posted on by Stefaan Verhulst

Introduction to a special issue of Data and Policy (Open Access) by Richard Benjamins, Jeanine Vos, and Stefaan Verhulst: “More than a year into the COVID-19 pandemic, the damage is still unfolding. While some countries have recently managed to gain an element of control through aggressive vaccine campaigns, much of the developing world — South and Southeast Asia in particular — remain in a state of crisis. Given what we now know about the global nature of this disease and the potential for mutant versions to develop and spread, a crisis anywhere is cause for concern everywhere. The world remains very much in the grip of this public health crisis.

From the beginning, there has been hope that data and technology could offer solutions to help inform the government’s response strategy and decision-making. Many of the expectations have been focused on mobile data analytics in particular, whereby mobile network operators create mobility insights and decision-support tools generated from anonymized and aggregated telco data. This stems both from a growing group of mobile network operators having significantly invested in systems and capabilities to develop such products and services for public and private sector customers. As well as their value having been demonstrated in addressing different global challenges, ranging from models to better understand the spread of Zika in Brazil to interactive dashboards to aid emergency services during earthquakes and floods in Japan. Yet despite these experiences, many governments across the world still have limited awareness, capabilities and resources to leverage these tools, in their efforts to limit the spread of COVID-19 using non-pharmaceutical interventions (NPI), both from a medical and economic point of view.

Today, we release the first batch of papers of a special collection of Data & Policy that examines both the potential of mobile data, as well as the challenges faced in delivering these tools to inform government decision-making. Consisting of 5 papers from 33 researchers and experts from academia, industry and government, the articles cover a wide range of geographies, including Europe, Argentina, Brazil, Ecuador, France, Gambia, Germany, Ghana, Austria, Belgium, and Spain. Responding to our call for case studies to illustrate the opportunities (and challenges) offered by mobile big data in the fight against COVID-19, the authors of these papers describe a number of examples of how mobile and mobile-related data have been used to address the medical, economic, socio-cultural and political aspects of the pandemic….(More)”.

Solving Public Problems

Curated on Posted on by Stefaan Verhulst

Book by Beth Simone Noveck (The GovLab): “The challenges societies face today, from inequality to climate change to systemic racism, cannot be solved with yesterday’s toolkit. Solving Public Problems shows how readers can take advantage of digital technology, data, and the collective wisdom of our communities to design and deliver powerful solutions to contemporary problems.  
 
Offering a radical rethinking of the role of the public servant and the skills of the public workforce, this book is about the vast gap between failing public institutions and the huge number of public entrepreneurs doing extraordinary things—and how to close that gap.  
 
Drawing on lessons learned from decades of advising global leaders and from original interviews and surveys of thousands of public problem solvers, Beth Simone Noveck provides a practical guide for public servants, community leaders, students, and activists to become more effective, equitable, and inclusive leaders and repair our troubled, twenty-first-century world….(More)”

Take the free online course presented by The GovLab at the NYU Tandon School of Engineering.

Using big data for insights into the gender digital divide for girls: A discussion paper

Curated on Posted on by Stefaan Verhulst
Using big data for insights into the gender digital divide for girls: A discussion paper

UNICEF paper: “This discussion paper describes the findings of a study that used big data as an alternative data source to understand the gender digital divide for under-18s. It describes 6 key insights gained from analysing big data from Facebook and Instagram platforms, and discusses how big data can be further used to contribute to the body of evidence for the gender digital divide for adolescent girls….(More)”

Citizen participation in budgeting and beyond: Deliberative Practices and their Impact in Contemporary Cases

Curated on Posted on by Stefaan Verhulst

Open Access book by Joanna Podgórska-Rykała and Jacek Sroka: “…The basic questions which the theory and practice of public policy try to answer is the question about desires in democratic conditions and at the same time an effective formula for balancing centralization and decentralization in decision-making processes. […]

Participatory budgeting, as one of possible variants of deliberation, is one of those phenomena of public life, the quality of which depends on the relations of the parties involved. The shape of these relationships only to a limited extent depends on the ways of their current practice, because these methods are causally conditioned, and the causes lie in cultural constructions. That is why these relations are not easy to study; it is difficult to reach that deep, because it is difficult to both model the conceptualization of the problem and the methodological approach to such research. These are one of the most difficult and, at the same time, the most promising research areas of public policy. We hope that this book will contribute to their partial exploration…(More)”.

ASEAN Data Management Framework

Curated on Posted on by Stefaan Verhulst

ASEAN Framework: “Due to the growing interactions between data, connected things and people, trust in data has become the pre-condition for fully realising the gains of digital transformation. SMEs are threading a fine line between balancing digital initiatives and concurrently managing data protection and customer privacy safeguards to ensure that these do not impede innovation. Therefore, there is a motivation to focus on digital data governance as it is critical to boost economic integration and technology adoption across all sectors in the ten ASEAN Member States (AMS).
To ensure that their data is appropriately managed and protected, organisations need to know what levels of technical, procedural and physical controls they need to put in place. The categorisation of datasets help organisations manage their data assets and put in place the right level of controls. This is applicable for both data at rest as well as data in transit. The establishment of an ASEAN Data Management Framework will promote sound data governance practices by helping organisations to discover the datasets they have, assign it with the appropriate categories, manage the data, protect it accordingly and all these while continuing to comply with relevant regulations. Improved governance and protection will instil trust in data sharing both between organisations and between countries, which will then promote the growth of trade and the flow of data among AMS and their partners in the digital economy….(More)”

Here Be Dragons – Maintaining Trust in the Technologized Public Sector

Curated on Posted on by Stefaan Verhulst

Paper by Balázs Bodó and Heleen Janssen: “Emerging technologies, such as AI systems, distributed ledgers, but also private e-commerce and telecommunication platforms have permeated every aspect of our social, economic, political relations. Various bodies of the state, from education, via law enforcement to healthcare also increasingly rely on technical components to provide cheap, efficient public services, and supposedly fair, transparent, disinterested, accountable public administration. Most of these technical components are provided by private parties who designed, developed, trained, and maintain the technical components of public infrastructures.
The rapid, and often unplanned, and uncontrolled technologization of public services (as happened, for example in the rapid adoption of distance learning and teleconferencing systems during the COVID lockdowns) inseparably link the perception of the quality, trustworthiness, effectiveness of public services and the public bodies which provision them to the successes and failures of their private, technological components: if the government’s welfare fraud AI system fails, it is the confidence in the governments which is ultimately hit.


In this contribution we explore how the use of potentially untrustworthy private technological systems in the public sector may affect the trust in government. We argue that citizens’ and business’ trust in government is a valuable asset, which came under assault from many dimensions. The increasing reliance on private technical components in government is in part a response to protect this trust, but in many cases, it opens up new forms of threats and vulnerabilities, because the trustworthiness of many of these private technical systems is, at best, questionable, particularly where it is deployed in the context of public sector trust contexts. We consider a number of policy options to protect the trust in government even if some of their technological components are fundamentally untrustworthy….(More)”.

Ethics and governance of artificial intelligence for health

Curated on Posted on by Stefaan Verhulst

The WHO guidance…”on Ethics & Governance of Artificial Intelligence for Health is the product of eighteen months of deliberation amongst leading experts in ethics, digital technology, law, human rights, as well as experts from Ministries of Health.  While new technologies that use artificial intelligence hold great promise to improve diagnosis, treatment, health research and drug development and to support governments carrying out public health functions, including surveillance and outbreak response, such technologies, according to the report, must put ethics and human rights at the heart of its design, deployment, and use.

The report identifies the ethical challenges and risks with the use of artificial intelligence of health, six consensus principles to ensure AI works to the public benefit of all countries. It also contains a set of recommendations that can ensure the governance of artificial intelligence for health maximizes the promise of the technology and holds all stakeholders – in the public and private sector – accountable and responsive to the healthcare workers who will rely on these technologies and the communities and individuals whose health will be affected by its use…(More)”

Pooling society’s collective intelligence helped fight COVID – it must help fight future crises too

Curated on Posted on by Stefaan Verhulst

Aleks Berditchevskaia and Kathy Peach at The Conversation: “A Global Pandemic Radar is to be created to detect new COVID variants and other emerging diseases. Led by the WHO, the project aims to build an international network of surveillance hubs, set up to share data that’ll help us monitor vaccine resistance, track diseases and identify new ones as they emerge.

This is undeniably a good thing. Perhaps more than any event in recent memory, the COVID pandemic has brought home the importance of pooling society’s collective intelligence and finding new ways to share that combined knowledge as quickly as possible.

At its simplest, collective intelligence is the enhanced capacity that’s created when diverse groups of people work together, often with the help of technology, to mobilise more information, ideas and knowledge to solve a problem. Digital technologies have transformed what can be achieved through collective intelligence in recent years – connecting more of us, augmenting human intelligence with machine intelligence, and helping us to generate new insights from novel sources of data.

So what have we learned over the last 18 months of collective intelligence pooling that can inform the Global Pandemic Radar? Building from the COVID crisis, what lessons will help us perfect disease surveillance and respond better to future crises?…(More)”

National strategies on Artificial Intelligence: A European perspective

Curated on Posted on by Stefaan Verhulst

Report by European Commission’s Joint Research Centre (JRC) and the OECD’s Science Technology and Innovation Directorate: “Artificial intelligence (AI) is transforming the world in many aspects. It is essential for Europe to consider how to make the most of the opportunities from this transformation and to address its challenges. In 2018 the European Commission adopted the Coordinated Plan on Artificial Intelligence that was developed together with the Member States to maximise the impact of investments at European Union (EU) and national levels, and to encourage synergies and cooperation across the EU.

One of the key actions towards these aims was an encouragement for the Member States to develop their national AI strategies.The review of national strategies is one of the tasks of AI Watch launched by the European Commission to support the implementation of the Coordinated Plan on Artificial Intelligence.

Building on the 2020 AI Watch review of national strategies, this report presents an updated review of national AI strategies from the EU Member States, Norway and Switzerland. By June 2021, 20 Member States and Norway had published national AI strategies, while 7 Member States were in the final drafting phase. Since the 2020 release of the AI Watch report, additional Member States – i.e. Bulgaria, Hungary, Poland, Slovenia, and Spain – published strategies, while Cyprus, Finland and Germany have revised the initial strategies.

This report provides an overview of national AI policies according to the following policy areas: Human capital, From the lab to the market, Networking, Regulation, and Infrastructure. These policy areas are consistent with the actions proposed in the Coordinated Plan on Artificial Intelligence and with the policy recommendations to governments contained in the OECD Recommendation on AI. The report also includes a section on AI policies to address societal challenges of the COVID-19 pandemic and climate change….(More)”.

Governance mechanisms for sharing of health data: An approach towards selecting attributes for complex discrete choice experiment studies

Curated on Posted on by Stefaan Verhulst

Paper by Jennifer Viberg Johansson: “Discrete Choice Experiment (DCE) is a well-established technique to elicit individual preferences, but it has rarely been used to elicit governance preferences for health data sharing.

The aim of this article was to describe the process of identifying attributes for a DCE study aiming to elicit preferences of citizens in Sweden, Iceland and the UK for governance mechanisms for digitally sharing different kinds of health data in different contexts.

A three-step approach was utilised to inform the attribute and level selection: 1) Attribute identification, 2) Attribute development and 3) Attribute refinement. First, we developed an initial set of potential attributes from a literature review and a workshop with experts. To further develop attributes, focus group discussions with citizens (n = 13), ranking exercises among focus group participants (n = 48) and expert interviews (n = 18) were performed. Thereafter, attributes were refined using group discussion (n = 3) with experts as well as cognitive interviews with citizens (n = 11).

The results led to the selection of seven attributes for further development: 1) level of identification, 2) the purpose of data use, 3) type of information, 4) consent, 5) new data user, 6) collector and 7) the oversight of data sharing. Differences were found between countries regarding the order of top three attributes. The process outlined participants’ conceptualisation of the chosen attributes, and what we learned for our attribute development phase.

This study demonstrates a process for selection of attributes for a (multi-country) DCE involving three stages: Attribute identification, Attribute development and Attribute refinement. This study can contribute to improve the ethical aspects and good practice of this phase in DCE studies. Specifically, it can contribute to the development of governance mechanisms in the digital world, where people’s health data are shared for multiple purposes….(More)”.