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)”.

Beyond the Valley

Curated on Posted on by Stefaan Verhulst

Book by Ramesh Srinivasan: “How to repair the disconnect between designers and users, producers and consumers, and tech elites and the rest of us: toward a more democratic internet.

In this provocative book, Ramesh Srinivasan describes the internet as both an enabler of frictionless efficiency and a dirty tangle of politics, economics, and other inefficient, inharmonious human activities. We may love the immediacy of Google search results, the convenience of buying from Amazon, and the elegance and power of our Apple devices, but it’s a one-way, top-down process. We’re not asked for our input, or our opinions—only for our data. The internet is brought to us by wealthy technologists in Silicon Valley and China. It’s time, Srinivasan argues, that we think in terms beyond the Valley.

Srinivasan focuses on the disconnection he sees between designers and users, producers and consumers, and tech elites and the rest of us. The recent Cambridge Analytica and Russian misinformation scandals exemplify the imbalance of a digital world that puts profits before inclusivity and democracy. In search of a more democratic internet, Srinivasan takes us to the mountains of Oaxaca, East and West Africa, China, Scandinavia, North America, and elsewhere, visiting the “design labs” of rural, low-income, and indigenous people around the world. He talks to a range of high-profile public figures—including Elizabeth Warren, David Axelrod, Eric Holder, Noam Chomsky, Lawrence Lessig, and the founders of Reddit, as well as community organizers, labor leaders, and human rights activists. To make a better internet, Srinivasan says, we need a new ethic of diversity, openness, and inclusivity, empowering those now excluded from decisions about how technologies are designed, who profits from them, and who are surveilled and exploited by them….(More)”

Could AI Drive Transformative Social Progress? What Would This Require?

Curated on Posted on by Stefaan Verhulst

Paper by Edward (Ted) A. Parson et al: “In contrast to popular dystopian speculation about the societal impacts of widespread AI deployment, we consider AI’s potential to drive a social transformation toward greater human liberty, agency, and equality. The impact of AI, like all technology, will depend on both properties of the technology and the economic, social, and political conditions of its deployment and use. We identify conditions of each type – technical characteristics and socio-political context – likely to be conducive to such large-scale beneficial impacts.

Promising technical characteristics include decision-making structures that are tentative and pluralistic, rather than optimizing a single-valued objective function under a single characterization of world conditions; and configuring the decision-making of AI-enabled products and services exclusively to advance the interests of their users, subject to relevant social values, not those of their developers or vendors. We explore various strategies and business models for developing and deploying AI-enabled products that incorporate these characteristics, including philanthropic seed capital, crowd-sourcing, open-source development, and sketch various possible ways to scale deployment thereafter….(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)”.

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)”.

Handbook of Research on Politics in the Computer Age

Curated on Posted on by Stefaan Verhulst

Book edited by Ashu M. G. Solo: “Technology and particularly the Internet have caused many changes in the realm of politics. Aspects of engineering, computer science, mathematics, or natural science can be applied to politics. Politicians and candidates use their own websites and social network profiles to get their message out. Revolutions in many countries in the Middle East and North Africa have started in large part due to social networking websites such as Facebook and Twitter. Social networking has also played a role in protests and riots in numerous countries. The mainstream media no longer has a monopoly on political commentary as anybody can set up a blog or post a video online. Now, political activists can network together online.

The Handbook of Research on Politics in the Computer Age is a pivotal reference source that serves to increase the understanding of methods for politics in the computer age, the effectiveness of these methods, and tools for analyzing these methods. The book includes research chapters on different aspects of politics with information technology, engineering, computer science, or math, from 27 researchers at 20 universities and research organizations in Belgium, Brazil, Cape Verde, Egypt, Finland, France, Hungary, Italy, Mexico, Nigeria, Norway, Portugal, and the United States of America. Highlighting topics such as online campaigning and fake news, the prospective audience includes, but is not limited to, researchers, political and public policy analysts, political scientists, engineers, computer scientists, political campaign managers and staff, politicians and their staff, political operatives, professors, students, and individuals working in the fields of politics, e-politics, e-government, new media and communication studies, and Internet marketing….(More)”.

Artificial Discretion as a Tool of Governance: A Framework for Understanding the Impact of Artificial Intelligence on Public Administration

Curated on Posted on by Stefaan Verhulst

Paper by Matthew M Young, Justin B Bullock, and Jesse D Lecy in Perspectives on Public Management and Governance: “Public administration research has documented a shift in the locus of discretion away from street-level bureaucrats to “systems-level bureaucracies” as a result of new information communication technologies that automate bureaucratic processes, and thus shape access to resources and decisions around enforcement and punishment. Advances in artificial intelligence (AI) are accelerating these trends, potentially altering discretion in public management in exciting and in challenging ways. We introduce the concept of “artificial discretion” as a theoretical framework to help public managers consider the impact of AI as they face decisions about whether and how to implement it. We operationalize discretion as the execution of tasks that require nontrivial decisions. Using Salamon’s tools of governance framework, we compare artificial discretion to human discretion as task specificity and environmental complexity vary. We evaluate artificial discretion with the criteria of effectiveness, efficiency, equity, manageability, and political feasibility. Our analysis suggests three principal ways that artificial discretion can improve administrative discretion at the task level: (1) increasing scalability, (2) decreasing cost, and (3) improving quality. At the same time, artificial discretion raises serious concerns with respect to equity, manageability, and political feasibility….(More)”.

Benefits of Open Data in Public Health

Curated on Posted on by Stefaan Verhulst

Paper by P. Huston, VL. Edge and E. Bernier: “Open Data is part of a broad global movement that is not only advancing science and scientific communication but also transforming modern society and how decisions are made. What began with a call for Open Science and the rise of online journals has extended to Open Data, based on the premise that if reports on data are open, then the generated or supporting data should be open as well. There have been a number of advances in Open Data over the last decade, spearheaded largely by governments. A real benefit of Open Data is not simply that single databases can be used more widely; it is that these data can also be leveraged, shared and combined with other data. Open Data facilitates scientific collaboration, enriches research and advances analytical capacity to inform decisions. In the human and environmental health realms, for example, the ability to access and combine diverse data can advance early signal detection, improve analysis and evaluation, inform program and policy development, increase capacity for public participation, enable transparency and improve accountability. However, challenges remain. Enormous resources are needed to make the technological shift to open and interoperable databases accessible with common protocols and terminology. Amongst data generators and users, this shift also involves a cultural change: from regarding databases as restricted intellectual property, to considering data as a common good. There is a need to address legal and ethical considerations in making this shift. Finally, along with efforts to modify infrastructure and address the cultural, legal and ethical issues, it is important to share the information equitably and effectively. While there is great potential of the open, timely, equitable and straightforward sharing of data, fully realizing the myriad of benefits of Open Data will depend on how effectively these challenges are addressed….(More)”.