Digital Equity 2.0: How to Close the Data Divide

Curated on Posted on by Stefaan Verhulst

Report by Gillian Diebold: “For the last decade, closing the digital divide, or the gap between those subscribing to broadband and those not subscribing, has been a top priority for policymakers. But high-speed Internet and computing device access are no longer the only barriers to fully participating and benefiting from the digital economy. Data is also increasingly essential, including in health care, financial services, and education. Like the digital divide, a gap has emerged between the data haves and the data have-nots, and this gap has introduced a new set of inequities: the data divide.

Policymakers have put a great deal of effort into closing the digital divide, and there is now near-universal acceptance of the notion that obtaining widespread Internet access generates social and economic benefits. But closing the data divide has received little attention. Moreover, efforts to improve data collection are typically overshadowed by privacy advocates’ warnings against collecting any data. In fact, unlike the digital divide, many ignore the data divide or argue that the way to close it is to collect vastly less data.1 But without substantial efforts to increase data representation and access, certain individuals and communities will be left behind in an increasingly data-driven world.

This report describes the multipronged efforts needed to address digital inequity. For the digital divide, policymakers have expanded digital connectivity, increased digital literacy, and improved access to digital devices. For the data divide, policymakers should similarly take a holistic approach, including by balancing privacy and data innovation, increasing data collection efforts across a wide array of fronts, enhancing access to data, improving data quality, and improving data analytics efforts. Applying lessons from the digital divide to this new challenge will help policymakers design effective and efficient policy and create a more equitable and effective data economy for all Americans…(More)”.

3 barriers to successful data collaboratives

Curated on Posted on by Stefaan Verhulst

Article by Federico Bartolomucci: “Data collaboratives have proliferated in recent years as effective means of promoting the use of data for social good. This type of social partnership involves actors from the private, public, and not-for-profit sectors working together to leverage public or private data to enhance collective capacity to address societal and environmental challenges. The California Data Collaborative for instance, combines the data of numerous Californian water managers to enhance data-informed policy and decision making. 

But, in my years as a researcher studying more than a hundred cases of data collaboratives, I have observed widespread feelings of isolation among collaborating partners due to the absence of success-proven reference models. …Below, I provide an overview of three governance challenges faced by practitioners, as well as recommendations for addressing them. In doing so, I encourage every practitioner embarking on a data collaborative initiative to reflect on these challenges and create ad-hoc strategies to address them…

1. Overly relying on grant funding limits a collaborative’s options.

Data Collaboratives are typically conceived as not-for-profit projects, relying solely on grant funding from the founding partners. This is the case, for example, with TD1_Index, a global collaboration that seeks to gather data on Type 1 diabetes, raise awareness, and advance research on the topic. Although grant funding schemas work in some cases (like in that of T1D_Index), relying solely on grant funding makes a data collaborative heavily dependent on the willingness of one or more partners to sustain its activities and hinders its ability to achieve operational and decisional autonomy.

Operational and decisional autonomy indeed appears to be a beneficial condition for a collaborative to develop trust, involve other partners, and continuously adapt its activities and structure to external events—characteristics required for operating in a highly innovative sector.

Hybrid business models that combine grant funding with revenue-generating activities indicate a promising evolutionary path. The simplest way to do this is to monetize data analysis and data stewardship services. The ActNow Coalition, a U.S.-based not-for-profit organization, combines donations with client-funded initiatives in which the team provides data collection, analysis, and visualization services. Offering these types of services generates revenues for the collaborative and gaining access to them is among the most compelling incentives for partners to join the collaboration.

In studying data collaboratives around the world, two models emerge as most effective: (1) pay-per-use models, in which collaboration partners can access data-related services on demand (see Civity NL and their project Sniffer Bike) and (2) membership models, in which participation in the collaborative entitles partners to access certain services under predefined conditions (see the California Data Collaborative).

2. Demonstrating impact is key to a collaborative’s survival. 

As partners’ participation in data collaboratives is primarily motivated by a shared social purpose, the collaborative’s ability to demonstrate its efficacy in achieving its purpose means being able to defend its raison d’être. Demonstrating impact enables collaboratives to retain existing partners, renew commitments, and recruit new partners…(More)”.

Data Sharing Between Public and Private Sectors: When Local Governments Seek Information from the Sharing Economy.

Curated on Posted on by Stefaan Verhulst

Paper by the Centre for Information Policy Leadership: “…addresses the growing trend of localities requesting (and sometimes mandating) that data collected by the private sector be shared with the localities themselves. Such requests are generally not in the context of law enforcement or national security matters, but rather are part of an effort to further the public interest or promote a public good.

To the extent such requests are overly broad or not specifically tailored to the stated public interest, CIPL believes that the public sector’s adoption of accountability measures—which CIPL has repeatedly promoted for the private sector—can advance responsible data sharing practices between the two sectors. It can also strengthen the public’s confidence in data-driven initiatives that seek to improve their communities…(More)”.

Spatial data trusts: an emerging governance framework for sharing spatial data

Curated on Posted on by Stefaan Verhulst

Paper by Nenad Radosevic et al: “Data Trusts are an important emerging approach to enabling the much wider sharing of data from many different sources and for many different purposes, backed by the confidence of clear and unambiguous data governance. Data Trusts combine the technical infrastructure for sharing data with the governance framework of a legal trust. The concept of a data Trust applied specifically to spatial data offers significant opportunities for new and future applications, addressing some longstanding barriers to data sharing, such as location privacy and data sovereignty. This paper introduces and explores the concept of a ‘spatial data Trust’ by identifying and explaining the key functions and characteristics required to underpin a data Trust for spatial data. The work identifies five key features of spatial data Trusts that demand specific attention and connects these features to a history of relevant work in the field, including spatial data infrastructures (SDIs), location privacy, and spatial data quality. The conclusions identify several key strands of research for the future development of this rapidly emerging framework for spatial data sharing…(More)”.

Unlocking the Power of Data Refineries for Social Impact

Curated on Posted on by Stefaan Verhulst

Essay by Jason Saul & Kriss Deiglmeier: “In 2021, US companies generated $2.77 trillion in profits—the largest ever recorded in history. This is a significant increase since 2000 when corporate profits totaled $786 billion. Social progress, on the other hand, shows a very different picture. From 2000 to 2021, progress on the United Nations Sustainable Development Goals has been anemic, registering less than 10 percent growth over 20 years.

What explains this massive split between the corporate and the social sectors? One explanation could be the role of data. In other words, companies are benefiting from a culture of using data to make decisions. Some refer to this as the “data divide”—the increasing gap between the use of data to maximize profit and the use of data to solve social problems…

Our theory is that there is something more systemic going on. Even if nonprofit practitioners and policy makers had the budget, capacity, and cultural appetite to use data; does the data they need even exist in the form they need it? We submit that the answer to this question is a resounding no. Usable data doesn’t yet exist for the sector because the sector lacks a fully functioning data ecosystem to create, analyze, and use data at the same level of effectiveness as the commercial sector…(More)”.

Data Rivers: Carving Out the Public Domain in the Age of Generative AI

Curated on Posted on by Stefaan Verhulst

Paper by Sylvie Delacroix: “What if the data ecosystems that made the advent of generative AI possible are being undermined by those very tools? For tools such as GPT4 (it is but one example of a tool made possible by scraping data from the internet), the erection of IP ‘fences’ is an existential threat. European and British regulators are alert to it: so-called ‘text and data mining’ exceptions are at the heart of intense debates. In the US, these debates are taking place in court hearings structured around ‘fair use’. While the concerns of the corporations developing these tools are being heard, there is currently no reliable mechanism for members of the public to exert influence on the (re)-balancing of the rights and responsibilities that shape our ‘data rivers’. Yet the existential threat that stems from restricted public access to such tools is arguably greater.

When it comes to re-balancing the data ecosystems that made generative AI possible, much can be learned from age-old river management practices, with one important proviso: data not only carries traces of our past. It is also a powerful tool to envisage different futures. If data-powered technologies such as GPT4 are to live up to their potential, we would do well to invest in bottom-up empowerment infrastructure. Such infrastructure could not only facilitate the valorisation of and participation in the public domain. It could also help steer the (re)-development of ‘copyright as privilege’ in a way that is better able to address the varied circumstances of today’s original content creators…(More)”

Data property, data governance and Common European Data Spaces

Curated on Posted on by Stefaan Verhulst

Paper by Thomas Margoni, Charlotte Ducuing and Luca Schirru: “The Data Act proposal of February 2022 constitutes a central element of a broader and ambitious initiative of the European Commission (EC) to regulate the data economy through the erection of a new general regulatory framework for data and digital markets. The resulting framework may be represented as a model of governance between a pure market-driven model and a fully regulated approach, thereby combining elements that traditionally belong to private law (e.g., property rights, contracts) and public law (e.g., regulatory authorities, limitation of contractual freedom). This article discusses the role of (intellectual) property rights as well as of other forms of rights allocation in data legislation with particular attention to the Data Act proposal. We argue that the proposed Data Act has the potential to play a key role in the way in which data, especially privately held data, may be accessed, used, and shared. Nevertheless, it is only by looking at the whole body of data (and data related) legislation that the broader plan for a data economy can be grasped in its entirety. Additionally, the Data Act proposal may also arguably reveal the elements for a transition from a property-based to a governance-based paradigm in the EU data strategy. Whereas elements of data governance abound, the stickiness of property rights and rhetoric seem however hard to overcome. The resulting regulatory framework, at least for now, is therefore an interesting but not always perfectly coordinated mix of both. Finally, this article suggests that the Data Act Proposal may have missed the chance to properly address the issue of data holders’ power and related information asymmetries, as well as the need for coordination mechanisms…(More)”.

End of data sharing could make Covid-19 harder to control, experts and high-risk patients warn

Curated on Posted on by Stefaan Verhulst

Article by Sam Whitehead: “…The federal government’s public health emergency that’s been in effect since January 2020 expires May 11. The emergency declaration allowed for sweeping changes in the U.S. health care system, like requiring state and local health departments, hospitals, and commercial labs to regularly share data with federal officials.

But some shared data requirements will come to an end and the federal government will lose access to key metrics as a skeptical Congress seems unlikely to grant agencies additional powers. And private projects, like those from The New York Times and Johns Hopkins University, which made covid data understandable and useful for everyday people, stopped collecting data in March.

Public health legal scholars, data experts, former and current federal officials, and patients at high risk of severe covid outcomes worry the scaling back of data access could make it harder to control covid.

There have been improvements in recent years, such as major investments in public health infrastructure and updated data reporting requirements in some states. But concerns remain that the overall shambolic state of U.S. public health data infrastructure could hobble the response to any future threats.

“We’re all less safe when there’s not the national amassing of this information in a timely and coherent way,” said Anne Schuchat, former principal deputy director of the Centers for Disease Control and Prevention.

A lack of data in the early days of the pandemic left federal officials, like Schuchat, with an unclear picture of the rapidly spreading coronavirus. And even as the public health emergency opened the door for data-sharing, the CDC labored for months to expand its authority.

Eventually, more than a year into the pandemic, the CDC gained access to data from private health care settings, such as hospitals and nursing homes, commercial labs, and state and local health departments…(More)”. See also: Why we still need data to understand the COVID-19 pandemic

Harnessing Data Innovation for Migration Policy: A Handbook for Practitioners

Curated on Posted on by Stefaan Verhulst

Report by IOM: “The Practitioners’ Handbook provides first-hand insights into why and how non-traditional data sources can contribute to better understanding migration-related phenomena. The Handbook aims to (a) bridge the practical and technical aspects of using data innovations in migration statistics, (a) demonstrate the added value of using new data sources and innovative methodologies to analyse key migration topics that may be hard to fully grasp using traditional data sources, and (c) identify good practices in addressing issues of data access and collaboration with multiple stakeholders (including the private sector), ethical standards, and security and data protection issues…(More)” See also Big Data for Migration Alliance.

Whose data commons? Whose city?

Curated on Posted on by Stefaan Verhulst

Blog by Gijs van Maanen and Anna Artyushina: “In 2020, the notion of data commons became a staple of the new European Data Governance Strategy, which envisions data cooperatives as key players of the European Union’s (EU) emerging digital market. In this new legal landscape, public institutions, businesses, and citizens are expected to share their data with the licensed data-governance entities that will oversee its responsible reuse. In 2022, the Open Future Foundation released several white papers where the NGO (non-govovernmental organisation) detailed a vision for the publicly governed and funded EU level data commons. Some academic researchers see data commons as a way to break the data silos maintained and exploited by Big Tech and, potentially, dismantle surveillance capitalism.

In this blog post, we discuss data commons as a concept and practice. Our argument here is that, for data commons to become a (partial) solution to the issues caused by data monopolies, they need to be politicised. As smart city scholar Shannon Mattern pointedly argues, the city is not a computer. This means that digitization and datafication of our cities involves making choices about what is worth digitising and whose interests are prioritised. These choices and their implications must be foregrounded when we discuss data commons or any emerging forms of data governance. It is important to ask whose data is made common and, subsequently, whose city we will end up living in. ..(More)”