Paper by Heike Schweitzer and Robert Welker: “The paper strives to systematise the debate on access to data from a competition policy angle. At the outset, two general policy approaches to access to data are distinguished: a “private control of data” approach versus an “open access” approach. We argue that, when it comes to private sector data, the “private control of data” approach is preferable. According to this approach, the “whether” and “how” of data access should generally be left to the market. However, public intervention can be justified by significant market failures. We discuss the presence of such market failures and the policy responses, including, in particular, competition policy responses, with a view to three different data access scenarios: access to data by co-generators of usage data (Scenario 1); requests for access to bundled or aggregated usage data by third parties vis-à-vis a service or product provider who controls such datasets, with the goal to enter complementary markets (Scenario 2); requests by firms to access the large usage data troves of the Big Tech online platforms for innovative purposes (Scenario 3). On this basis we develop recommendations for data access policies….(More)”.
Not fit for Purpose: A critical analysis of the ‘Five Safes’
Paper by Chris Culnane, Benjamin I. P. Rubinstein, and David Watts: “Adopted by government agencies in Australia, New Zealand, and the UK as policy instrument or as embodied into legislation, the ‘Five Safes’ framework aims to manage risks of releasing data derived from personal information. Despite its popularity, the Five Safes has undergone little legal or technical critical analysis. We argue that the Fives Safes is fundamentally flawed: from being disconnected from existing legal protections and appropriation of notions of safety without providing any means to prefer strong technical measures, to viewing disclosure risk as static through time and not requiring repeat assessment. The Five Safes provides little confidence that resulting data sharing is performed using ‘safety’ best practice or for purposes in service of public interest….(More)”.
The CARE Principles for Indigenous Data Governance
Paper by Stephanie Russo Carroll et al: “Concerns about secondary use of data and limited opportunities for benefit-sharing have focused attention on the tension that Indigenous communities feel between (1) protecting Indigenous rights and interests in Indigenous data (including traditional knowledges) and (2) supporting open data, machine learning, broad data sharing, and big data initiatives. The International Indigenous Data Sovereignty Interest Group (within the Research Data Alliance) is a network of nation-state based Indigenous data sovereignty networks and individuals that developed the ‘CARE Principles for Indigenous Data Governance’ (Collective Benefit, Authority to Control, Responsibility, and Ethics) in consultation with Indigenous Peoples, scholars, non-profit organizations, and governments. The CARE Principles are people– and purpose-oriented, reflecting the crucial role of data in advancing innovation, governance, and self-determination among Indigenous Peoples. The Principles complement the existing data-centric approach represented in the ‘FAIR Guiding Principles for scientific data management and stewardship’ (Findable, Accessible, Interoperable, Reusable). The CARE Principles build upon earlier work by the Te Mana Raraunga Maori Data Sovereignty Network, US Indigenous Data Sovereignty Network, Maiam nayri Wingara Aboriginal and Torres Strait Islander Data Sovereignty Collective, and numerous Indigenous Peoples, nations, and communities. The goal is that stewards and other users of Indigenous data will ‘Be FAIR and CARE.’ In this first formal publication of the CARE Principles, we articulate their rationale, describe their relation to the FAIR Principles, and present examples of their application….(More)” See also Selected Readings on Indigenous Data Sovereignty.
To mitigate the costs of future pandemics, establish a common data space
Article by Stephanie Chin and Caitlin Chin: “To improve data sharing during global public health crises, it is time to explore the establishment of a common data space for highly infectious diseases. Common data spaces integrate multiple data sources, enabling a more comprehensive analysis of data based on greater volume, range, and access. At its essence, a common data space is like a public library system, which has collections of different types of resources from books to video games; processes to integrate new resources and to borrow resources from other libraries; a catalog system to organize, sort, and search through resources; a library card system to manage users and authorization; and even curated collections or displays that highlight themes among resources.
Even before the COVID-19 pandemic, there was significant momentum to make critical data more widely accessible. In the United States, Title II of the Foundations for Evidence-Based Policymaking Act of 2018, or the OPEN Government Data Act, requires federal agencies to publish their information online as open data, using standardized, machine-readable data formats. This information is now available on the federal data.gov catalog and includes 50 state- or regional-level data hubs and 47 city- or county-level data hubs. In Europe, the European Commission released a data strategy in February 2020 that calls for common data spaces in nine sectors, including healthcare, shared by EU businesses and governments.
Going further, a common data space could help identify outbreaks and accelerate the development of new treatments by compiling line list incidence data, epidemiological information and models, genome and protein sequencing, testing protocols, results of clinical trials, passive environmental monitoring data, and more.
Moreover, it could foster a common understanding and consensus around the facts—a prerequisite to reach international buy-in on policies to address situations unique to COVID-19 or future pandemics, such as the distribution of medical equipment and PPE, disruption to the tourism industry and global supply chains, social distancing or quarantine, and mass closures of businesses….(More). See also Call for Action for a Data Infrastructure to tackle Pandemics and other Dynamic Threats.
AI’s Wide Open: A.I. Technology and Public Policy
Paper by Lauren Rhue and Anne L. Washington: “Artificial intelligence promises predictions and data analysis to support efficient solutions for emerging problems. Yet, quickly deploying AI comes with a set of risks. Premature artificial intelligence may pass internal tests but has little resilience under normal operating conditions. This Article will argue that regulation of early and emerging artificial intelligence systems must address the management choices that lead to releasing the system into production. First, we present examples of premature systems in the Boeing 737 Max, the 2020 coronavirus pandemic public health response, and autonomous vehicle technology. Second, the analysis highlights relevant management practices found in our examples of premature AI. Our analysis suggests that redundancy is critical to protecting the public interest. Third, we offer three points of context for premature AI to better assess the role of management practices.
AI in the public interest should: 1) include many sensors and signals; 2) emerge from a broad range of sources; and 3) be legible to the last person in the chain. Finally, this Article will close with a series of policy suggestions based on this analysis. As we develop regulation for artificial intelligence, we need to cast a wide net to identify how problems develop within the technologies and through organizational structures….(More)”.
Trace Labs
Trace Labs is a nonprofit organization whose mission is to accelerate
the family reunification of missing persons while training members in
the trade craft of open source intelligence (OSINT)….We crowdsource open source intelligence through both the Trace Labs OSINT Search Party CTFs and Ongoing Operations with our global community. Our highly skilled intelligence analysts then triage the data collected to produce actionable intelligence reports on each missing persons subject. These intelligence reports allow the law enforcement agencies that we work with the ability to quickly see any new details required to reopen a cold case and/or take immediate action on a missing subject.(More)”
The Potential Role Of Open Data In Mitigating The COVID-19 Pandemic: Challenges And Opportunities
Essay by Sunyoung Pyo, Luigi Reggi and Erika G. Martin: “…There is one tool for the COVID-19 response that was not as robust in past pandemics: open data. For about 15 years, a “quiet open data revolution” has led to the widespread availability of governmental data that are publicly accessible, available in multiple formats, free of charge, and with unlimited use and distribution rights. The underlying logic of open data’s value is that diverse users including researchers, practitioners, journalists, application developers, entrepreneurs, and other stakeholders will synthesize the data in novel ways to develop new insights and applications. Specific products have included providing the public with information about their providers and health care facilities, spotlighting issues such as high variation in the cost of medical procedures between facilities, and integrating food safety inspection reports into Yelp to help the public make informed decisions about where to dine. It is believed that these activities will in turn empower health care consumers and improve population health.
Here, we describe several use cases whereby open data have already been used globally in the COVID-19 response. We highlight major challenges to using these data and provide recommendations on how to foster a robust open data ecosystem to ensure that open data can be leveraged in both this pandemic and future public health emergencies…(More)” See also Repository of Open Data for Covid19 (OECD/TheGovLab)
Open Infrastructure Map
“Open Infrastructure Map is a view of the world’s hidden infrastructure mapped in the OpenStreetMap database.
By and large, this data isn’t exposed on the main OSM map, so I built Open Infrastructure Map to visualise it…(More).”
Taming Complexity
Martin Reeves , Simon Levin , Thomas Fink and Ania Levina at Harvard Business Review: “….“Complexity” is one of the most frequently used terms in business but also one of the most ambiguous. Even in the sciences it has numerous definitions. For our purposes, we’ll define it as a large number of different elements (such as specific technologies, raw materials, products, people, and organizational units) that have many different connections to one another. Both qualities can be a source of advantage or disadvantage, depending on how they’re managed.
Let’s look at their strengths. To begin with, having many different elements increases the resilience of a system. A company that relies on just a few technologies, products, and processes—or that is staffed with people who have very similar backgrounds and perspectives—doesn’t have many ways to react to unforeseen opportunities and threats. What’s more, the redundancy and duplication that also characterize complex systems typically give them more buffering capacity and fallback options.
Ecosystems with a diversity of elements benefit from adaptability. In biology, genetic diversity is the grist for natural selection, nature’s learning mechanism. In business, as environments shift, sustained performance requires new offerings and capabilities—which can be created by recombining existing elements in fresh ways. For example, the fashion retailer Zara introduces styles (combinations of components) in excess of immediate needs, allowing it to identify the most popular products, create a tailored selection from them, and adapt to fast-changing fashion as a result.
Another advantage that complexity can confer on natural ecosystems is better coordination. That’s because the elements are often highly interconnected. Flocks of birds or herds of animals, for instance, share behavioral protocols that connect the members to one another and enable them to move and act as a group rather than as an uncoordinated collection of individuals. Thus they realize benefits such as collective security and more-effective foraging.
Finally, complexity can confer inimitability. Whereas individual elements may be easily copied, the interrelationships among multiple elements are hard to replicate. A case in point is Apple’s attempt in 2012 to compete with Google Maps. Apple underestimated the complexity of Google’s offering, leading to embarrassing glitches in the initial versions of its map app, which consequently struggled to gain acceptance with consumers. The same is true of a company’s strategy: If its complexity makes it hard to understand, rivals will struggle to imitate it, and the company will benefit….(More)”.
Third Wave of Open Data
Paper (and site) by Stefaan G. Verhulst, Andrew Young, Andrew J. Zahuranec, Susan Ariel Aaronson, Ania Calderon, and Matt Gee on “How To Accelerate the Re-Use of Data for Public Interest Purposes While Ensuring Data Rights and Community Flourishing”: “The paper begins with a description of earlier waves of open data. Emerging from freedom of information laws adopted over the last half century, the First Wave of Open Data brought about newfound transparency, albeit one only available on request to an audience largely composed of journalists, lawyers, and activists.
The Second Wave of Open Data, seeking to go beyond access to public records and inspired by the open source movement, called upon national governments to make their data open by default. Yet, this approach too had its limitations, leaving many data silos at the subnational level and in the private sector untouched..
The Third Wave of Open Data seeks to build on earlier successes and take into account lessons learned to help open data realize its transformative potential. Incorporating insights from various data experts, the paper describes the emergence of a Third Wave driven by the following goals:
- Publishing with Purpose by matching the supply of data with the demand for it, providing assets that match public interests;
- Fostering Partnerships and Data Collaboration by forging relationships with community-based organizations, NGOs, small businesses, local governments, and others who understand how data can be translated into meaningful real-world action;
- Advancing Open Data at the Subnational Level by providing resources to cities, municipalities, states, and provinces to address the lack of subnational information in many regions.
- Prioritizing Data Responsibility and Data Rights by understanding the risks of using (and not using) data to promote and preserve the public’s general welfare.
Riding the Wave
Achieving these goals will not be an easy task and will require investments and interventions across the data ecosystem. The paper highlights eight actions that decision and policy makers can take to foster more equitable, impactful benefits… (More) (PDF) “