We are finally getting better at predicting organized conflict

Curated on Posted on by Stefaan Verhulst

Tate Ryan-Mosley at MIT Technology Review: “People have been trying to predict conflict for hundreds, if not thousands, of years. But it’s hard, largely because scientists can’t agree on its nature or how it arises. The critical factor could be something as apparently innocuous as a booming population or a bad year for crops. Other times a spark ignites a powder keg, as with the assassination of Archduke Franz Ferdinand of Austria in the run-up to World War I.

Political scientists and mathematicians have come up with a slew of different methods for forecasting the next outbreak of violence—but no single model properly captures how conflict behaves. A study published in 2011 by the Peace Research Institute Oslo used a single model to run global conflict forecasts from 2010 to 2050. It estimated a less than .05% chance of violence in Syria. Humanitarian organizations, which could have been better prepared had the predictions been more accurate, were caught flat-footed by the outbreak of Syria’s civil war in March 2011. It has since displaced some 13 million people.

Bundling individual models to maximize their strengths and weed out weakness has resulted in big improvements. The first public ensemble model, the Early Warning Project, launched in 2013 to forecast new instances of mass killing. Run by researchers at the US Holocaust Museum and Dartmouth College, it claims 80% accuracy in its predictions.

Improvements in data gathering, translation, and machine learning have further advanced the field. A newer model called ViEWS, built by researchers at Uppsala University, provides a huge boost in granularity. Focusing on conflict in Africa, it offers monthly predictive readouts on multiple regions within a given state. Its threshold for violence is a single death.

Some researchers say there are private—and in some cases, classified—predictive models that are likely far better than anything public. Worries that making predictions public could undermine diplomacy or change the outcome of world events are not unfounded. But that is precisely the point. Public models are good enough to help direct aid to where it is needed and alert those most vulnerable to seek safety. Properly used, they could change things for the better, and save lives in the process….(More)”.

Citizen Engagement in Energy Efficiency Retrofit of Public Housing Buildings: A Lisbon Case Study

Curated on Posted on by 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)”.

Artificial intelligence: From expert-only to everywhere

Curated on Posted on by Stefaan Verhulst

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)”.

New Directions in Public Opinion

Curated on Posted on by Stefaan Verhulst

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)”.

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)”