Open Government Data for Inclusive Development

Curated on Posted on by Stefaan Verhulst

Chapter by F. van Schalkwyk and M,  Cañares in  “Making Open Development Inclusive”, MIT Press by Matthew L. Smith and Ruhiya Kris Seward (Eds):  “This chapter examines the relationship between open government data and social inclusion. Twenty-eight open data initiatives from the Global South are analyzed to find out how and in what contexts the publication of open government data tend to result in the inclusion of habitually marginalized communities in governance processes such that they may lead better lives.

The relationship between open government data and social inclusion is examined by presenting an analysis of the outcomes of open data projects. This analysis is based on a constellation of factors that were identified as having a bearing on open data initiatives with respect to inclusion. The findings indicate that open data can contribute to an increase in access and participation— both components of inclusion. In these cases, this particular finding indicates that a more open, participatory approach to governance practice is taking root. However, the findings also show that access and participation approaches to open government data have, in the cases studied here, not successfully disrupted the concentration of power in political and other networks, and this has placed limits on open data’s contribution to a more inclusive society.

The chapter starts by presenting a theoretical framework for the analysis of the relationship between open data and inclusion. The framework sets out the complex relationship between social actors, information and power in the network society. This is critical, we suggest, in developing a realistic analysis of the contexts in which open data activates its potential for
transformation. The chapter then articulates the research question and presents the methodology used to operationalize those questions. The findings and discussion section that follows examines the factors affecting the relationship between open data and inclusion, and how these factors
are observed to play out across several open data initiatives in different contexts. The chapter ends with concluding remarks and an attempt to synthesize the insights that emerged in the preceding sections….(More)”.

Better Data for Doing Good: Responsible Use of Big Data and Artificial Intelligence

Curated on Posted on by Stefaan Verhulst

Report by the World Bank: “Describes opportunities for harnessing the value of big data and artificial intelligence (AI) for social good and how new families of AI algorithms now make it possible to obtain actionable insights automatically and at scale. Beyond internet business or commercial applications, multiple examples already exist of how big data and AI can help achieve shared development objectives, such as the 2030 Agenda for Sustainable Development and the Sustainable Development Goals (SDGs). But ethical frameworks in line with increased uptake of these new technologies remain necessary—not only concerning data privacy but also relating to the impact and consequences of using data and algorithms. Public recognition has grown concerning AI’s potential to create both opportunities for societal benefit and risks to human rights. Development calls for seizing the opportunity to shape future use as a force for good, while at the same time ensuring the technologies address inequalities and avoid widening the digital divide….(More)”.

Artificial Intelligence: Public-Private Partnerships join forces to boost AI progress in Europe

Curated on Posted on by Stefaan Verhulst

European Commission Press Release: “…the Big Data Value Association and euRobotics agreed to cooperate more in order to boost the advancement of artificial intelligence’s (AI) in Europe. Both associations want to strengthen their collaboration on AI in the future. Specifically by:

  • Working together to boost European AI, building on existing industrial and research communities and on results of the Big Data Value PPP and SPARC PPP. This to contribute to the European Commission’s ambitious approach to AI, backed up with a drastic increase investment, reaching €20 billion total public and private funding in Europe until 2020.
  • Enabling joint-pilots, for example, to accelerate the use and integration of big data, robotics and AI technologies in different sectors and society as a whole
  • Exchanging best practices and approaches from existing and future projects of the Big Data PPP and the SPARC PPP
  • Contributing to the European Digital Single Market, developing strategic roadmaps and  position papers

This Memorandum of Understanding between the PPPs follows the European Commission’s approach to AI presented in April 2018 and the Declaration of Cooperation on Artificial Intelligence signed by all 28 Member States and Norway. This Friday 7 December the Commission will present its EU coordinated plan….(More)”.

Data Collaboration, Pooling and Hoarding under Competition Law

Curated on Posted on by Stefaan Verhulst

Paper by Bjorn Lundqvist: “In the Internet of Things era devices will monitor and collect data, whilst device producing firms will store, distribute, analyse and re-use data on a grand scale. Great deal of data analytics will be used to enable firms to understand and make use of the collected data. The infrastructure around the collected data is controlled and access to the data flow is thus restricted on technical, but also on legal grounds. Legally, the data are being obscured behind a thicket of property rights, including intellectual property rights. Therefore, there is no general “data commons” for everyone to enjoy.

If firms would like to combine data, they need to give each other access either by sharing, trading, or pooling the data. On the one hand, industry-wide pooling of data could increase efficiency of certain services, and contribute to the innovation of other services, e.g., think about self-driven cars or personalized medicine. On the other hand, firms combining business data may use the data, not to advance their services or products, but to collude, to exclude competitors or to abuse their market position. Indeed by combining their data in a pool, they can gain market power, and, hence, the ability to violate competition law. Moreover, we also see firms hoarding data from various source creating de facto data pools. This article will discuss what implications combining data in data pools by firms might have on competition, and when competition law should be applicable. It develops the idea that data pools harbour great opportunities, whilst acknowledging that there are still risks to take into consideration, and to regulate….(More)”.

Using Mobile Network Data for Development: How it works

Curated on Posted on by Stefaan Verhulst

Blog by Derval Usher and Darren Hanniffy: “…We aim to equip decision makers with data tools so that they have access to the analysis on the fly. But to help this scale we need progress in three areas:

1. The framework to support Shared Value partnerships.

2. Shared understanding of The Proposition and the benefits for all parties.

3. Access to finance and a funding strategy, designing-in innovation.

1. Any Public-Private Partnership should be aligned to achieve impact centered on the SDGs through a Shared Value / Inclusive Business approach. Mobile network operators are consumed with the challenge of maintaining or upgrading their infrastructure, driving device sales and sustaining their agent networks to reach the last mile. Measuring impact against the SDGs has not been a priority. Mobile network operators tend not to seek out partnerships with traditional development donors or development implementers. But there is a growing realisation of the potential and the need to partner. It’s important to move from a service level transactional relationship to a strategic partnership approach.

Private sector partners have been fundamental to the success of UN Global Pulse as these companies are often the custodians of the big data sets from which we develop valuable development and humanitarian insights. Although in previous years our private sector partners were framed primarily as data philanthropists, we are beginning to see a shift in the relationship to one of shared value. Our work generates public value and also insights that can enhance business operations. This shared value model is attracting more private enterprises to engage and to explore their own data, and more broadly to investigate the value of their networks and data as part of the data innovation ecosystem, which the Global Pulse lab network will build on as we move forward.

2. Partners need to be more propositional and less charitable. They need to recognise the fact that earning profit may help ensure the sustainability of digital platforms and services that offer developmental impact. Through partnership we can attract innovative finance, deliver mobile for development programmes, measure impact and create affordable commercial solutions to development challenges that become sustainable by design. Pulse Lab Jakarta and Digicel have been flexible with one another which is important as this partnership has not always been a priority for either side all the time. But we believe in unlocking the power of mobile data for development and therefore continue to make progress.

3. Development and commercial strategies should be more aligned to create an enabling environment. Currently they are not. Private sector needs to become a strategic partner to development where multi-annual development funds align with commercial strategy. Mobile network operators continue to invest in their network particularly in developing countries and the digital platform is coming into being in the markets where Digicel operates. But the platform is new and experience is limited within governments, the development community and indeed even within mobile network operators.

We need to see donors actively engage during the development of multi-annual funding facilities….(More)”.

Why We Need to Audit Algorithms

Curated on Posted on by Stefaan Verhulst

James Guszcza, Iyad Rahwan, Will Bible, Manuel Cebrian and Vic Katyal at Harvard Business Review: “Algorithmic decision-making and artificial intelligence (AI) hold enormous potential and are likely to be economic blockbusters, but we worry that the hype has led many people to overlook the serious problems of introducing algorithms into business and society. Indeed, we see many succumbing to what Microsoft’s Kate Crawford calls “data fundamentalism” — the notion that massive datasets are repositories that yield reliable and objective truths, if only we can extract them using machine learning tools. A more nuanced view is needed. It is by now abundantly clear that, left unchecked, AI algorithms embedded in digital and social technologies can encode societal biases, accelerate the spread of rumors and disinformation, amplify echo chambers of public opinion, hijack our attention, and even impair our mental wellbeing.

Ensuring that societal values are reflected in algorithms and AI technologies will require no less creativity, hard work, and innovation than developing the AI technologies themselves. We have a proposal for a good place to start: auditing. Companies have long been required to issue audited financial statements for the benefit of financial markets and other stakeholders. That’s because — like algorithms — companies’ internal operations appear as “black boxes” to those on the outside. This gives managers an informational advantage over the investing public which could be abused by unethical actors. Requiring managers to report periodically on their operations provides a check on that advantage. To bolster the trustworthiness of these reports, independent auditors are hired to provide reasonable assurance that the reports coming from the “black box” are free of material misstatement. Should we not subject societally impactful “black box” algorithms to comparable scrutiny?

Indeed, some forward thinking regulators are beginning to explore this possibility. For example, the EU’s General Data Protection Regulation (GDPR) requires that organizations be able to explain their algorithmic decisions. The city of New York recently assembled a task force to study possible biases in algorithmic decision systems. It is reasonable to anticipate that emerging regulations might be met with market pull for services involving algorithmic accountability.

So what might an algorithm auditing discipline look like? First, it should adopt a holistic perspective. Computer science and machine learning methods will be necessary, but likely not sufficient foundations for an algorithm auditing discipline. Strategic thinking, contextually informed professional judgment, communication, and the scientific method are also required.

As a result, algorithm auditing must be interdisciplinary in order for it to succeed….(More)”.

Reimagining Public-Private Partnerships: Four Shifts and Innovations in Sharing and Leveraging Private Assets and Expertise for the Public Good

Curated on Posted on by Stefaan Verhulst

Blog by Stefaan G. Verhulst and Andrew J. Zahuranec: “For years, public-private partnerships (PPPs) have promised to help governments do more for less. Yet, the discussion and experimentation surrounding PPPs often focus on outdated models and narratives, and the field of experimentation has not fully embraced the opportunities provided by an increasingly networked and data-rich private sector.

Private-sector actors (including businesses and NGOs) have expertise and assets that, if brought to bear in collaboration with the public sector, could spur progress in addressing public problems or providing public services. Challenges to date have largely involved the identification of effective and legitimate means for unlocking the public value of private-sector expertise and assets. Those interested in creating public value through PPPs are faced with a number of questions, including:

  • How do we broaden and deepen our understanding of PPPs in the 21st Century?
  • How can we innovate and improve the ways that PPPs tap into private-sector assets and expertise for the public good?
  • How do we connect actors in the PPP space with open governance developments and practices, especially given that PPPs have not played a major role in the governance innovation space to date?

The PPP Knowledge Lab defines a PPP as a “long-term contract between a private party and a government entity, for providing a public asset or service, in which the private party bears significant risk and management responsibility and remuneration is linked to performance.”…

To maximize the value of PPPs, we don’t just need new tools or experiments but new models for using assets and expertise in different sectors. We need to bring that capacity to public problems.

At the latest convening of the MacArthur Foundation Research Network on Opening Governance, Network members and experts from across the field tried to chart this new course by exploring questions about the future of PPPs.

The group explored the new research and thinking that enables many new types of collaboration beyond the typical “contract” based approaches. Through their discussions, Network members identified four shifts representing ways that cross-sector collaboration could evolve in the future:

  1. From Formal to Informal Trust Mechanisms;
  2. From Selection to Iterative and Inclusive Curation;
  3. From Partnership to Platform; and
  4. From Shared Risk to Shared Outcome….(More)”.
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Advancing Open Data for Open Governance in Asia

Curated on Posted on by Stefaan Verhulst

Paper by Michael P. Cañares: “The record of countries in the region in terms of transparency and accountability is dismal. In the latest Corruption Perceptions Index released by Transparency International, more than half of the country in the region scored below 50, with at least a quarter of these are countries considered with systemic corruption problems. Nevertheless, there have been significant attempts of several countries to install transparency measures and project a commitment towards greater openness. At least a dozen of countries has right to information laws that provide citizens’ fundamental access to government information and several have installed open data policies and are implementing e-government programs or practices. But access of citizens to data and information to hold governments to account, demand for better services, and strengthen citizen participation in governance remain elusive.

The Open Government Partnership (OGP) is a multilateral initiative that aims to secure concrete commitments from governments to promote transparency, empower citizens, fight corruption, and harness new technologies to strengthen governance. OGP’s vision is that more governments become more transparent, more accountable, and more responsive to their own citizens, with the goal of improving the quality of governance, as well as the quality of services that citizens receive. Since its inception in 2011, OGP today brings together 75 countries and 15 subnational governments with over 2,500 commitments to make their governments more open and accountable. In Asia, only the governments of Indonesia, the Philippines, and South Korea are participating countries along with two subnational pilots, Seoul and Bojonegoro. These governments have launched initiatives to involve citizens in the planning and budgeting processes, proactively disclose budget and other public financial information, and engage citizens in monitoring of public service delivery. But these countries remain the exception rather than the norm….(More)”.

Quantum Information Science: Applications, Global Research and Development, and Policy Considerations

Curated on Posted on by Stefaan Verhulst

Report from the Congressional Research Service: “Quantum information science (QIS) combines elements of mathematics, computer science, engineering, and physical sciences, and has the potential to provide capabilities far beyond what is possible with the most advanced technologies available today.
Although much of the press coverage of QIS has been devoted to quantum computing, there is more to QIS. Many experts divide QIS technologies into three application areas:

  • Sensing and metrology,
  • Communications, and
  • Computing and simulation.

… Today, QIS is a component of the National Strategic Computing Initiative (Presidential Executive Order 13702), which was established in 2015. Most recently, in September 2018, the National Science and Technology Council issued the National Strategic Overview for Quantum Information Science. The policy opportunities identified in this strategic overview include:

  • choosing a science-first approach to QIS,
  • creating a “quantum-smart” workforce,
  • deepening engagement with the quantum industry,
  • providing critical infrastructure,
  • maintaining national security and economic growth, and
  • advancing international cooperation.

This report provides an overview of QIS technologies: sensing and metrology, communications, and computing and simulation. It also includes examples of existing and potential future applications; brief summaries of funding and selected R&D initiatives in the United States and elsewhere around the world; a description of U.S. congressional activity; and a discussion of related policy considerations….(More)”.

What difference does data make? Data management and social change

Curated on Posted on by Stefaan Verhulst

Paper by Morgan E. Currie and Joan M. Donovan: “The purpose of this paper is to expand on emergent data activism literature to draw distinctions between different types of data management practices undertaken by groups of data activists.

The authors offer three case studies that illuminate the data management strategies of these groups. Each group discussed in the case studies is devoted to representing a contentious political issue through data, but their data management practices differ in meaningful ways. The project Making Sense produces their own data on pollution in Kosovo. Fatal Encounters collects “missing data” on police homicides in the USA. The Environmental Data Governance Initiative hopes to keep vulnerable US data on climate change and environmental injustices in the public domain.

In analysing our three case studies, the authors surface how temporal dimensions, geographic scale and sociotechnical politics influence their differing data management strategies….(More)”.