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)”
Report and interactive map by CityLab, Bloomberg Philanthropies and the OECD: “New Innovation helps local governments create an ecosystem that promotes experimentation and creativity to improve the public welfare of residents in cities around the world.
City governments are ushering in a new era of local public sector innovation that promotes experimentation and flexibility, and also takes into account the social needs of citizens to manage evolving urban systems. The goal of this report is to understand how municipalities can enhance their ability to use innovation to deliver better results for their residents….
This site identifies and shares how cities around the world are investing in innovation, to ensure they’re constantly assessing and improving how they’re tackling problems and improving the lives of residents. This map is based on an initial survey of cities in OECD and non-OECD countries. The city information reflects data gathered from the city administration at the time of the survey….(More)”
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)”
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)”.
Paper by Peter A.Johnson, Pamela J.Robinson, andSimonePhilpot: “In the current push for smart city programs around the world, there is a significant focus on enabling transactions between citizen and government. Though traditionally there have always been transactional elements between government and citizen, for example payment of taxes in exchange for services, or voting in exchange for representation, the rise of modern smartphone and smart city technologies have further enabled micro-transactions between citizen, government, and information broker. We conceptualize how the modern smart city, as both envisaged and enacted, incorporates the citizen not necessarily as a whole actor, but as a series of micro-transactions encoded on the real-time landscape of the city. This transactional citizen becomes counted by smart city sensors and integrated into smart city decision-making through the use of certain preferred platforms.
To approach this shift from traditional forms of citizen/city interaction towards micro-transactions, we conceptualize four broad modes of transaction; type (intentional contribution), tweet (intermediated by third party), tap (convened or requested transaction), and pass (ambient transaction based on movement). These four modes are used to frame critical questions of how citizens interact with government in the emerging age of the smart city, and how these interactions impact the relationship between citizen and government, introducing new avenues for private sector influence….(More)”
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)”.
The RSA: “As decisions are increasingly automated or made with the help of artificial intelligence, machines are becoming more influential in our lives. These machines are generating a range of predictions, such as the likelihood of a defendant reoffending or the job performance of a candidate based on video interview. In some cases, these predictions could lead to positive outcomes, such as less biased decisions or greater political engagement, but there are also risks that come with ceding power or outsourcing human judgment to a machine.
The RSA’s Forum for Ethical AI ran a citizens’ jury to explore the use of AI in decision-making. We convened participants to grapple with the ethical issues raised by this application of AI under different circumstances and enter into a deliberative dialogue about how companies, organisations, and public institutions should respond.
This report presents a toolkit for organisations seeking to deploy their own ethical processes around the proliferation of AI….(More)”.
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)”.
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)”.
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)”.