Stefaan Verhulst

Paper by Catarina Rolim and Ricardo Gomes: “In Portugal, there are about 120 thousand social housing and a large share of them are in need of some kind of rehabilitation. Alongside the technical challenge associated with the retrofit measures implementation, there is the challenge of involving the citizens in adopting more energy conscious behaviors. Within the Sharing Cities project and, specifically in the case of social housing retrofit, engagement activities with the tenants are being promoted, along with participation from city representatives, decision makers, stakeholders, and among others. This paper will present a methodology outlined to evaluate the impact of retrofit measures considering the citizen as a crucial retrofit stakeholder. The approach ranges from technical analysis and data monitoring but also conveys activities such as educational and training sessions, interviews, surveys, workshops, public events, and focus groups. These will be conducted during the different stages of project implementation; the definition process, during deployment and beyond deployment of solutions….(More)”.

Deloitte: “…AI consists of multiple technologies. At its foundation are machine learning and its more complex offspring, deep-learning neural networks. These technologies animate AI applications such as computer vision, natural language processing, and the ability to harness huge troves of data to make accurate predictions and to unearth hidden insights (see sidebar, “The parlance of AI technologies”). The recent excitement around AI stems from advances in machine learning and deep-learning neural networks—and the myriad ways these technologies can help companies improve their operations, develop new offerings, and provide better customer service at a lower cost.

The trouble with AI, however, is that to date, many companies have lacked the expertise and resources to take full advantage of it. Machine learning and deep learning typically require teams of AI experts, access to large data sets, and specialized infrastructure and processing power. Companies that can bring these assets to bear then need to find the right use cases for applying AI, create customized solutions, and scale them throughout the company. All of this requires a level of investment and sophistication that takes time to develop, and is out of reach for many….

These tech giants are using AI to create billion-dollar services and to transform their operations. To develop their AI services, they’re following a familiar playbook: (1) find a solution to an internal challenge or opportunity; (2) perfect the solution at scale within the company; and (3) launch a service that quickly attracts mass adoption. Hence, we see Amazon, Google, Microsoft, and China’s BATs launching AI development platforms and stand-alone applications to the wider market based on their own experience using them.

Joining them are big enterprise software companies that are integrating AI capabilities into cloud-based enterprise software and bringing them to the mass market. Salesforce, for instance, integrated its AI-enabled business intelligence tool, Einstein, into its CRM software in September 2016; the company claims to deliver 1 billion predictions per day to users. SAP integrated AI into its cloud-based ERP system, S4/HANA, to support specific business processes such as sales, finance, procurement, and the supply chain. S4/HANA has around 8,000 enterprise users, and SAP is driving its adoption by announcing that the company will not support legacy SAP ERP systems past 2025.

A host of startups is also sprinting into this market with cloud-based development tools and applications. These startups include at least six AI “unicorns,” two of which are based in China. Some of these companies target a specific industry or use case. For example, Crowdstrike, a US-based AI unicorn, focuses on cybersecurity, while Benevolent.ai uses AI to improve drug discovery.

The upshot is that these innovators are making it easier for more companies to benefit from AI technology even if they lack top technical talent, access to huge data sets, and their own massive computing power. Through the cloud, they can access services that address these shortfalls—without having to make big upfront investments. In short, the cloud is democratizing access to AI by giving companies the ability to use it now….(More)”.

Book edited by Adam J. Berinsky: “The 2016 elections called into question the accuracy of public opinion polling while tapping into new streams of public opinion more widely. The third edition of this well-established text addresses these questions and adds new perspectives to its authoritative line-up. The hallmark of this book is making cutting-edge research accessible and understandable to students and general readers. Here we see a variety of disciplinary approaches to public opinion reflected including psychology, economics, sociology, and biology in addition to political science. An emphasis on race, gender, and new media puts the elections of 2016 into context and prepares students to look ahead to 2020 and beyond.

New to the third edition:

• Includes 2016 election results and their implications for public opinion polling going forward.

• Three new chapters have been added on racializing politics, worldview politics, and the modern information environment….(More)”.

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)”.

Report by Marianna Mazucatto: “This report, Governing Missions, looks at the ‘how’: how to implement and govern a mission-oriented process so that it unleashes the full creativity and ambition potential of R&I policy-making; and how it crowds-in investments from across Europe in the process. The focus is on 3 key questions:

• How to engage citizens in codesigning, co-creating, co-implementing
  and co-assessing missions?
• What are the public sector capabilities and instruments needed to foster a dynamic innovation ecosystem, including the ability of civil servants to welcome experimentation and help governments work outside silos?
• How can mission-oriented finance and funding leverage and crowd-in other forms of finance, galvanising innovation across actors (public, private and third sector), different manufacturing and service sectors, and across national and transnational levels?…(More)”.

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)”.

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)”.

UK Policy Lab: “Open justice is the principle that ‘justice should not only be done, but should manifestly and undoubtedly be seen to be done’(1). It is a very well established principle within our justice system, however new digital tools and approaches are creating new opportunities and potential challenges which necessitate significant rethinking on how open justice is delivered.

In this context, HM Courts & Tribunal Service (HMCTS) wanted to consider how the principle of open justice should be delivered in the future. As well as seeking input from those who most commonly work with courtrooms, like judges, court staff and legal professionals, they also wanted to explore a range of public views. HMCTS asked us to create a methodology which could spark a wide-ranging conversation about open justice, collecting diverse and divergent perspectives….

We approached this challenge by using speculative design to explore possible and desirable futures with citizens. In this blog we will share what we did (including how you can re-use our materials and approach), what we’ve learned, and what we’ll be experimenting with from here.

What we did

We ran 4 groups of 10 to 12 participants each. We spent the first 30 minutes discussing what participants understood and thought about Open Justice in the present. We spent the next 90 minutes using provocations to immerse them in a range of fictional futures, in which the justice system is accessed through a range of digital platforms.

The provocations were designed to:

• engage even those with no prior interest, experience or knowledge of Open Justice
• be reusable
• not look like ‘finished’ government policy – we wanted to find out more about desirable outcomes
• as far as possible, provoke discussion without leading

Using provocations to help participants think about the future allowed us to distill common principles which HMCTS can use when designing specific delivery mechanisms.

We hope the conversation can continue. HMCTS have published the provocations on their website. We encourage people to reuse them, or to use them to create their own….(More)”.

Paper by Maria Savona: “This note attempts a systematisation of different pieces of literature that underpin the recent policy and academic debate on the value of data. It mainly poses foundational questions around the definition, economic nature and measurement of data value, and discusses the opportunity to redistribute it. It then articulates a framework to compare ways of implementing redistribution, distinguishing between data as capital, data as labour or data as an intellectual property. Each of these raises challenges, revolving around the notions of data property and data rights, that are also briefly discussed. The note concludes by indicating areas for policy considerations and a research agenda to shape the future structure of data governance more at large….(More)”.

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)”.