OMB rethinks ‘protected’ or ‘open’ data binary with upcoming Evidence Act guidance

Curated on Posted on by Stefaan Verhulst

Jory Heckman at Federal News Network: “The Foundations for Evidence-Based Policymaking Act has ordered agencies to share their datasets internally and with other government partners — unless, of course, doing so would break the law.

Nearly a year after President Donald Trump signed the bill into law, agencies still have only a murky idea of what data they can share, and with whom. But soon, they’ll have more nuanced options of ranking the sensitivity of their datasets before sharing them out to others.

Chief Statistician Nancy Potok said the Office of Management and Budget will soon release proposed guidelines for agencies to provide “tiered” access to their data, based on the sensitivity of that information….

OMB, as part of its Evidence Act rollout, will also rethink how agencies ensure protected access to data for research. Potok said agency officials expect to pilot a single application governmentwide for people seeking access to sensitive data not available to the public.

The pilot resembles plans for a National Secure Data Service envisioned by the Commission on Evidence-Based Policymaking, an advisory group whose recommendations laid the groundwork for the Evidence Act.

“As a state-of-the-art resource for improving government’s capacity to use the data it already collects, the National Secure Data Service will be able to temporarily link existing data and provide secure access to those data for exclusively statistical purposes in connection with approved projects,” the commission wrote in its 2017 final report.

In an effort to strike a balance between access and privacy, Potok said OMB has also asked agencies to provide a list of the statutes that prohibit them from sharing data amongst themselves….(More)”.

Geolocation Data for Pattern of Life Analysis in Lower-Income Countries

Curated on Posted on by Stefaan Verhulst

Report by Eduardo Laguna-Muggenburg, Shreyan Sen and Eric Lewandowski: “Urbanization processes in the developing world are often associated with the creation of informal settlements. These areas frequently have few or no public services exacerbating inequality even in the context of substantial economic growth.

In the past, the high costs of gathering data through traditional surveying methods made it challenging to study how these under-served areas evolve through time and in relation to the metropolitan area to which they belong. However, the advent of mobile phones and smartphones in particular presents an opportunity to generate new insights on these old questions.

In June 2019, Orbital Insight and the United Nations Development Programme (UNDP) Arab States Human Development Report team launched a collaborative pilot program assessing the feasibility of using geolocation data to understand patterns of life among the urban poor in Cairo, Egypt.

The objectives of this collaboration were to assess feasibility (and conditionally pursue preliminary analysis) of geolocation data to create near-real time population density maps, understand where residents of informal settlements tend to work during the day, and to classify universities by percentage of students living in informal settlements.

The report is organized as follows. In Section 2 we describe the data and its limitations. In Section 3 we briefly explain the methodological background. Section 4 summarizes the insights derived from the data for the Egyptian context. Section 5 concludes….(More)”.

A Constitutional Right to Public Information

Curated on Posted on by Stefaan Verhulst

Paper by Chad G. Marzen: “In the wake of the 2013 United States Supreme Court decision of McBurney v. Young (569 U.S. 221), this Article calls for policymakers at the federal and state levels to ensure governmental records remain open and accessible to the public. It urges policymakers to call not only for strengthening of the Freedom of Information Act and the various state public records law, but to pursue an amendment to the United States Constitution providing a right to public information.

This Article proposes a draft of such an amendment:

The right to public information, being a necessary and vital part of democracy, shall be a fundamental right of the people. The right of the people to inspect and/or copy records of government, and to be provided notice of and attend public meetings of government, shall not unreasonably be restricted.

This Article analyzes the benefits of the amendment and concludes the enshrining of the right to public information in both the United States Constitution as well as various state constitutions will ensure greater access of public records and documents to the general public, consistent with the democratic value of open, transparent government….(More)”.

Algorithmic futures: The life and death of Google Flu Trends

Curated on Posted on by Stefaan Verhulst

Vincent Duclos in Medicine Anthropology Theory: “In the last few years, tracking systems that harvest web data to identify trends, calculate predictions, and warn about potential epidemic outbreaks have proliferated. These systems integrate crowdsourced data and digital traces, collecting information from a variety of online sources, and they promise to change the way governments, institutions, and individuals understand and respond to health concerns. This article examines some of the conceptual and practical challenges raised by the online algorithmic tracking of disease by focusing on the case of Google Flu Trends (GFT). Launched in 2008, GFT was Google’s flagship syndromic surveillance system, specializing in ‘real-time’ tracking of outbreaks of influenza. GFT mined massive amounts of data about online search behavior to extract patterns and anticipate the future of viral activity. But it did a poor job, and Google shut the system down in 2015. This paper focuses on GFT’s shortcomings, which were particularly severe during flu epidemics, when GFT struggled to make sense of the unexpected surges in the number of search queries. I suggest two reasons for GFT’s difficulties. First, it failed to keep track of the dynamics of contagion, at once biological and digital, as it affected what I call here the ‘googling crowds’. Search behavior during epidemics in part stems from a sort of viral anxiety not easily amenable to algorithmic anticipation, to the extent that the algorithm’s predictive capacity remains dependent on past data and patterns. Second, I suggest that GFT’s troubles were the result of how it collected data and performed what I call ‘epidemic reality’. GFT’s data became severed from the processes Google aimed to track, and the data took on a life of their own: a trackable life, in which there was little flu left. The story of GFT, I suggest, offers insight into contemporary tensions between the indomitable intensity of collective life and stubborn attempts at its algorithmic formalization.Vincent DuclosIn the last few years, tracking systems that harvest web data to identify trends, calculate predictions, and warn about potential epidemic outbreaks have proliferated. These systems integrate crowdsourced data and digital traces, collecting information from a variety of online sources, and they promise to change the way governments, institutions, and individuals understand and respond to health concerns. This article examines some of the conceptual and practical challenges raised by the online algorithmic tracking of disease by focusing on the case of Google Flu Trends (GFT). Launched in 2008, GFT was Google’s flagship syndromic surveillance system, specializing in ‘real-time’ tracking of outbreaks of influenza. GFT mined massive amounts of data about online search behavior to extract patterns and anticipate the future of viral activity. But it did a poor job, and Google shut the system down in 2015. This paper focuses on GFT’s shortcomings, which were particularly severe during flu epidemics, when GFT struggled to make sense of the unexpected surges in the number of search queries. I suggest two reasons for GFT’s difficulties. First, it failed to keep track of the dynamics of contagion, at once biological and digital, as it affected what I call here the ‘googling crowds’. Search behavior during epidemics in part stems from a sort of viral anxiety not easily amenable to algorithmic anticipation, to the extent that the algorithm’s predictive capacity remains dependent on past data and patterns. Second, I suggest that GFT’s troubles were the result of how it collected data and performed what I call ‘epidemic reality’. GFT’s data became severed from the processes Google aimed to track, and the data took on a life of their own: a trackable life, in which there was little flu left. The story of GFT, I suggest, offers insight into contemporary tensions between the indomitable intensity of collective life and stubborn attempts at its algorithmic formalization….(More)”.

Innovation Partnerships: An effective but under-used tool for buying innovation

Curated on Posted on by Stefaan Verhulst

Claire Gamage at Challenging Procurement: “…in an era where demand for public sector services increases as budgets decrease, the public sector should start to consider alternative routes to procurement. …

What is the Innovation Partnership procedure?

In a nutshell, it is essentially a procurement process combined with an R&D contract. Authorities are then able to purchase the ‘end result’ of the R&D exercise, without having to undergo a new procurement procedure. Authorities may choose to appoint a number of partners to participate in the R&D phase, but may subsequently only purchase one/some of those solutions.

Why does this procedure result in more innovative solutions?

The procedure was designed to drive innovation. Indeed, it may only be used in circumstances where a solution is not already available on the open market. Therefore, participants in the Innovation Partnership will be asked to create something which does not already exist and should be tailored towards solving a particular problem or ‘challenge’ set by the authority.

This procedure may also be particularly attractive to SMEs/start-ups, who often find it easier to innovate in comparison with their larger competitors and therefore the purchasing authority is perhaps likely to obtain a more innovative product or service.

One of the key advantages of an Innovation Partnership is that the R&D phase is separate to the subsequent purchase of the solution. In other words, the authority is not (usually) under any obligation to purchase the ‘end result’ of the R&D exercise, but has the option to do so if it wishes. Therefore, it may be easier to discourage internal stakeholders from imposing selection criteria which inadvertently exclude SMEs/start-ups (e.g. minimum turnover requirements, parent company guarantees etc.), as the authority is not committed to actually purchasing at the end of the procurement process which will select the innovation partner(s)….(More)”.

Leveraging Private Data for Public Good: A Descriptive Analysis and Typology of Existing Practices

Curated on Posted on by Stefaan Verhulst

New report by Stefaan Verhulst, Andrew Young, Michelle Winowatan. and Andrew J. Zahuranec: “To address the challenges of our times, we need both new solutions and new ways to develop those solutions. The responsible use of data will be key toward that end. Since pioneering the concept of “data collaboratives” in 2015, The GovLab has studied and experimented with innovative ways to leverage private-sector data to tackle various societal challenges, such as urban mobility, public health, and climate change.

While we have seen an uptake in normative discussions on how data should be shared, little analysis exists of the actual practice. This paper seeks to address that gap and seeks to answer the following question: What are the variables and models that determine functional access to private sector data for public good? In Leveraging Private Data for Public Good: A Descriptive Analysis and Typology of Existing Practices, we describe the emerging universe of data collaboratives and develop a typology of six practice areas. Our goal is to provide insight into current applications to accelerate the creation of new data collaboratives. The report outlines dozens of examples, as well as a set of recommendations to enable more systematic, sustainable, and responsible data collaboration….(More)”

User Data as Public Resource: Implications for Social Media Regulation

Curated on Posted on by Stefaan Verhulst

Paper by Philip Napoli: “Revelations about the misuse and insecurity of user data gathered by social media platforms have renewed discussions about how best to characterize property rights in user data. At the same time, revelations about the use of social media platforms to disseminate disinformation and hate speech have prompted debates over the need for government regulation to assure that these platforms serve the public interest. These debates often hinge on whether any of the established rationales for media regulation apply to social media. This article argues that the public resource rationale that has been utilized in traditional media regulation in the United States applies to social media.

The public resource rationale contends that, when a media outlet utilizes a public resource—such as the broadcast spectrum, or public rights of way—the outlet must abide by certain public interest obligations that may infringe upon its First Amendment rights. This article argues that aggregate user data can be conceptualized as a public resource that triggers the application of a public interest regulatory framework to social media sites and other digital platforms that derive their revenue from the gathering, sharing, and monetization of massive aggregations of user data….(More)”.

Internet of Water

Curated on Posted on by Stefaan Verhulst

About: “Water is the essence of life and vital to the well-being of every person, economy, and ecosystem on the planet. But around the globe and here in the United States, water challenges are mounting as climate change, population growth, and other drivers of water stress increase. Many of these challenges are regional in scope and larger than any one organization (or even states), such as the depletion of multi-state aquifers, basin-scale flooding, or the wide-spread accumulation of nutrients leading to dead zones. Much of the infrastructure built to address these problems decades ago, including our data infrastructure, are struggling to meet these challenges. Much of our water data exists in paper formats unique to the organization collecting the data. Often, these organizations existed long before the personal computer was created (1975) or the internet became mainstream (mid 1990’s). As organizations adopted data infrastructure in the late 1990’s, it was with the mindset of “normal infrastructure” at the time. It was built to last for decades, rather than adapt with rapid technological changes. 

New water data infrastructure with new technologies that enable data to flow seamlessly between users and generate information for real-time management are needed to meet our growing water challenges. Decision-makers need accurate, timely data to understand current conditions, identify sustainability problems, illuminate possible solutions, track progress, and adapt along the way. Stakeholders need easy-to-understand metrics of water conditions so they can make sure managers and policymakers protect the environment and the public’s water supplies. The water community needs to continually improve how they manage this complex resource by using data and communicating information to support decision-making. In short, a sustained effort is required to accelerate the development of open data and information systems to support sustainable water resources management. The Internet of Water (IoW) is designed to be just such an effort….(More)”.

To What Extent Does the EU General Data Protection Regulation (GDPR) Apply to Citizen Scientist-led Health Research with Mobile Devices?

Curated on Posted on by Stefaan Verhulst

Article by Edward Dove and Jiahong Chen: “In this article, we consider the possible application of the European General Data Protection Regulation (GDPR) to “citizen scientist”-led health research with mobile devices. We argue that the GDPR likely does cover this activity, depending on the specific context and the territorial scope. Remaining open questions that result from our analysis lead us to call for a lex specialis that would provide greater clarity and certainty regarding the processing of health data for research purposes, including by these non-traditional researchers…(More)”.

Overbooked and Overlooked: Machine Learning and Racial Bias in Medical Appointment Scheduling

Curated on Posted on by Stefaan Verhulst

Paper by Michele Samorani et al: “Machine learning is often employed in appointment scheduling to identify the patients with the greatest no-show risk, so as to schedule them into overbooked slots, and thereby maximize the clinic performance, as measured by a weighted sum of all patients’ waiting time and the provider’s overtime and idle time. However, if the patients with the greatest no-show risk belong to the same demographic group, then that demographic group will be scheduled in overbooked slots disproportionately to the general population. This is problematic because patients scheduled in those slots tend to have a worse service experience than the other patients, as measured by the time they spend in the waiting room. Such negative experience may decrease patient’s engagement and, in turn, further increase no-shows. Motivated by the real-world case of a large specialty clinic whose black patients have a higher no-show probability than non-black patients, we demonstrate that combining machine learning with scheduling optimization causes racial disparity in terms of patient waiting time. Our solution to eliminate this disparity while maintaining the benefits derived from machine learning consists of explicitly including the objective of minimizing racial disparity. We validate our solution method both on simulated data and real-world data, and find that racial disparity can be completely eliminated with no significant increase in scheduling cost when compared to the traditional predictive overbooking framework….(More)”.