The Social Afterlife

Curated on Posted on by Stefaan Verhulst

Paper by Andrew Gilden: “Death is not what it used to be. With the rise of social media and advances in digital technology, postmortem decision-making increasingly involves difficult questions about the ongoing social presence of the deceased. Should a Twitter account keep tweeting? Should a YouTube singer keep singing? Should Tinder photos be swiped left for the very last time? The traditional touchstones of effective estate planning — reducing transaction costs and maximizing estate value — do little to guide this new social afterlife. Managing a person’s legacy has shifted away from questions of financial investment and asset management to questions of emotional and cultural stewardship. This Article brings together the diverse areas of law that shape a person’s legacy and develops a new framework for addressing the evolving challenges of legacy stewardship

This Article makes two main contributions. First, it identifies and critically examines the four models of stewardship that currently structure the laws of legacy: (1) the “freedom of disposition” model dominant in the laws of wills and trusts, (2) the “family inheritance” model dominant in copyright law, (3) the “public domain” model dominant in many states’ publicity rights laws, and (4) the “consumer contract” model dominant in over forty states’ new digital assets laws. Second, this Article develops a new stewardship model, which it calls the “decentered decedent.” The decentered decedent model recognizes that individuals occupy heterogenous social contexts, and it channels postmortem decision-making into each of those contexts. Unlike existing stewardship models, this new model does not try to centralize stewardship decisions in any one stakeholder — the family, the public, the market, or even the decedent themselves. Instead, the decentered decedent model distributes stewardship across the diverse, dispersed communities that we all leave behind….(More)”.

AI Global Surveillance Technology

Curated on Posted on by Stefaan Verhulst

Carnegie Endowment: “Artificial intelligence (AI) technology is rapidly proliferating around the world. A growing number of states are deploying advanced AI surveillance tools to monitor, track, and surveil citizens to accomplish a range of policy objectives—some lawful, others that violate human rights, and many of which fall into a murky middle ground.

In order to appropriately address the effects of this technology, it is important to first understand where these tools are being deployed and how they are being used.

To provide greater clarity, Carnegie presents an AI Global Surveillance (AIGS) Index—representing one of the first research efforts of its kind. The index compiles empirical data on AI surveillance use for 176 countries around the world. It does not distinguish between legitimate and unlawful uses of AI surveillance. Rather, the purpose of the research is to show how new surveillance capabilities are transforming the ability of governments to monitor and track individuals or systems. It specifically asks:

  • Which countries are adopting AI surveillance technology?
  • What specific types of AI surveillance are governments deploying?
  • Which countries and companies are supplying this technology?

Learn more about our findings and how AI surveillance technology is spreading rapidly around the globe….(More)”.

Real-time flu tracking. By monitoring social media, scientists can monitor outbreaks as they happen.

Curated on Posted on by Stefaan Verhulst

Charles Schmidt at Nature: “Conventional influenza surveillance describes outbreaks of flu that have already happened. It is based on reports from doctors, and produces data that take weeks to process — often leaving the health authorities to chase the virus around, rather than get on top of it.

But every day, thousands of unwell people pour details of their symptoms and, perhaps unknowingly, locations into search engines and social media, creating a trove of real-time flu data. If such data could be used to monitor flu outbreaks as they happen and to make accurate predictions about its spread, that could transform public-health surveillance.

Powerful computational tools such as machine learning and a growing diversity of data streams — not just search queries and social media, but also cloud-based electronic health records and human mobility patterns inferred from census information — are making it increasingly possible to monitor the spread of flu through the population by following its digital signal. Now, models that track flu in real time and forecast flu trends are making inroads into public-health practice.

“We’re becoming much more comfortable with how these models perform,” says Matthew Biggerstaff, an epidemiologist who works on flu preparedness at the US Centers for Disease Control and Prevention (CDC) in Atlanta, Georgia.

In 2013–14, the CDC launched the FluSight Network, a website informed by digital modelling that predicts the timing, peak and short-term intensity of the flu season in ten regions of the United States and across the whole country. According to Biggerstaff, flu forecasting helps responders to plan ahead, so they can be ready with vaccinations and communication strategies to limit the effects of the virus. Encouraged by progress in the field, the CDC announced in January 2019 that it will spend US$17.5 million to create a network of influenza-forecasting centres of excellence, each tasked with improving the accuracy and communication of real-time forecasts.

The CDC is leading the way on digital flu surveillance, but health agencies elsewhere are following suit. “We’ve been working to develop and apply these models with collaborators using a range of data sources,” says Richard Pebody, a consultant epidemiologist at Public Health England in London. The capacity to predict flu trajectories two to three weeks in advance, Pebody says, “will be very valuable for health-service planning.”…(More)”.

The Internet Relies on People Working for Free

Curated on Posted on by Stefaan Verhulst

Owen Williams at OneZero: “When you buy a product like Philips Hue’s smart lights or an iPhone, you probably assume the people who wrote their code are being paid. While that’s true for those who directly author a product’s software, virtually every tech company also relies on thousands of bits of free code, made available through “open-source” projects on sites like GitHub and GitLab.

Often these developers are happy to work for free. Writing open-source software allows them to sharpen their skills, gain perspectives from the community, or simply help the industry by making innovations available at no cost. According to Google, which maintains hundreds of open-source projects, open source “enables and encourages collaboration and the development of technology, solving real-world problems.”

But when software used by millions of people is maintained by a community of people, or a single person, all on a volunteer basis, sometimes things can go horribly wrong. The catastrophic Heartbleed bug of 2014, which compromised the security of hundreds of millions of sites, was caused by a problem in an open-source library called OpenSSL, which relied on a single full-time developer not making a mistake as they updated and changed that code, used by millions. Other times, developers grow bored and abandon their projects, which can be breached while they aren’t paying attention.

It’s hard to demand that programmers who are working for free troubleshoot problems or continue to maintain software that they’ve lost interest in for whatever reason — though some companies certainly try. Not adequately maintaining these projects, on the other hand, makes the entire tech ecosystem weaker. So some open-source programmers are asking companies to pay, not for their code, but for their support services….(More)”.

Agora: Towards An Open Ecosystem for Democratizing Data Science & Artificial Intelligence

Curated on Posted on by Stefaan Verhulst

Paper by Jonas Traub et al: “Data science and artificial intelligence are driven by a plethora of diverse data-related assets including datasets, data streams, algorithms, processing software, compute resources, and domain knowledge. As providing all these assets requires a huge investment, data sciences and artificial intelligence are currently dominated by a small number of providers who can afford these investments. In this paper, we present a vision of a data ecosystem to democratize data science and artificial intelligence. In particular, we envision a data infrastructure for fine-grained asset exchange in combination with scalable systems operation. This will overcome lock-in effects and remove entry barriers for new asset providers. Our goal is to enable companies, research organizations, and individuals to have equal access to data, data science, and artificial intelligence. Such an open ecosystem has recently been put on the agenda of several governments and industrial associations. We point out the requirements and the research challenges as well as outline an initial data infrastructure architecture for building such a data ecosystem…(More)”.

Citizens need to know numbers

Curated on Posted on by Stefaan Verhulst

David Spiegelhalter at Aeon: “…Many criticised the Leave campaign for its claim that Britain sends the EU £350 million a week. When Boris Johnson repeated it in 2017 – by which time he was Foreign Secretary – the chair of the UK Statistics Authority (the official statistical watchdog) rebuked him, noting it was a ‘clear misuse of official statistics’. A private criminal prosecution was even made against Johnson for ‘misconduct in a public office’, but it was halted by the High Court.

The message on the bus had a strong emotional resonance with millions of people, even though it was essentially misinformation. The episode demonstrates both the power and weakness of statistics: they can be used to amplify an entire worldview, and yet they often do not stand up to scrutiny. This is why statistical literacy is so important – in an age in which data plays an ever-more prominent role in society, the ability to spot ways in which numbers can be misused, and to be able to deconstruct claims based on statistics, should be a standard civic skill.

Statistics are not cold hard facts – as Nate Silver writes in The Signal and the Noise (2012): ‘The numbers have no way of speaking for themselves. We speak for them. We imbue them with meaning.’ Not only has someone used extensive judgment in choosing what to measure, how to define crucial ideas, and to analyse them, but the manner in which they are communicated can utterly change their emotional impact. Let’s assume that £350 million is the actual weekly contribution to the EU. I often ask audiences to suggest what they would put on the side of the bus if they were on the Remain side. A standard option for making an apparently big number look small is to consider it as a proportion of an even bigger number: for example, the UK’s GDP is currently around £2.3 trillion, and so this contribution would comprise less than 1 per cent of GDP, around six months’ typical growth. An alternative device is to break down expenditure into smaller, more easily grasped units: for example, as there are 66 million people in the UK, £350 million a week is equivalent to around 75p a day, less than $1, say about the cost of a small packet of crisps (potato chips). If the bus had said: We each send the EU the price of a packet of crisps each day, the campaign might not have been so successful.

Numbers are often used to persuade rather than inform, statistical literacy needs to be improved, and so surely we need more statistics courses in schools and universities? Well, yes, but this should not mean more of the same. After years of researching and teaching statistical methods, I am not alone in concluding that the way in which we teach statistics can be counterproductive, with an overemphasis on mathematical foundations through probability theory, long lists of tests and formulae to apply, and toy problems involving, say, calculating the standard deviation of the weights of cod. The American Statistical Association’s Guidelines for Assessment and Instruction in Statistics Education (2016) strongly recommended changing the pedagogy of statistics into one based on problemsolving, real-world examples, and with an emphasis on communication….(More)”.

Experimental Innovation Policy

Curated on Posted on by Stefaan Verhulst

Paper by Albert Bravo-Biosca: “Experimental approaches are increasingly being adopted across many policy fields, but innovation policy has been lagging. This paper reviews the case for policy experimentation in this field, describes the different types of experiments that can be undertaken, discusses some of the unique challenges to the use of experimental approaches in innovation policy, and summarizes some of the emerging lessons, with a focus on randomized trials. The paper concludes describing how at the Innovation Growth Lab we have been working with governments across the OECD to help them overcome the barriers to policy experimentation in order to make their policies more impactful….(More)”.

The business case for integrating claims and clinical data

Curated on Posted on by Stefaan Verhulst

Claudia Williams at MedCityNews: “The path to value-based care is arduous. For health plans, their ability to manage care, assess quality, lower costs, and streamline reporting is directly impacted by access to clinical data. For providers, the same can be said due to their lack of access to claims data. 

Providers and health plans are increasingly demanding integrated claims and clinical data to drive and support value-based care programs. These organizations know that clinical and claims information from more than a single organization is the only way to get a true picture of patient care. From avoiding medication errors to enabling an evidence-based approach to treatment or identifying at-risk patients, the value of integrated claims and clinical data is immense — and will have far-reaching influence on both health outcomes and costs of care over time.

On July 30, Medicare announced the Data at the Point of Care pilot to share valuable claims data with Medicare providers in order to “fill in information gaps for clinicians, giving them a more structured and complete patient history with information like previous diagnoses, past procedures, and medication lists.” But that’s not the only example. To transition from fee-for-service to value-based care, providers and health plans have begun to partner with health data networks to access integrated clinical and claims data: 

Health plan adoption of integrated data strategy

A California health plan is partnering with one of the largest nonprofit health data networks in California, to better integrate clinical and claims data. …

Providers leveraging claims data to understand patient medication patterns 

Doctors using advanced health data networks typically see a full list of patients’ medications, derived from claims, when they treat them. With this information available, doctors can avoid dangerous drug to-drug interactions when they prescribe new medications. After a visit, they can also follow up and see if a patient actually filled a prescription and is still taking it….(More)”.

Guide to Mobile Data Analytics in Refugee Scenarios

Curated on Posted on by Stefaan Verhulst

Book edited Albert Ali Salah, Alex Pentland, Bruno Lepri and Emmanuel Letouzé: “After the start of the Syrian Civil War in 2011–12, increasing numbers of civilians sought refuge in neighboring countries. By May 2017, Turkey had received over 3 million refugees — the largest r efugee population in the world. Some lived in government-run camps near the Syrian border, but many have moved to cities looking for work and better living conditions. They faced problems of integration, income, welfare, employment, health, education, language, social tension, and discrimination. In order to develop sound policies to solve these interlinked problems, a good understanding of refugee dynamics is necessary.

This book summarizes the most important findings of the Data for Refugees (D4R) Challenge, which was a non-profit project initiated to improve the conditions of the Syrian refugees in Turkey by providing a database for the scientific community to enable research on urgent problems concerning refugees. The database, based on anonymized mobile call detail records (CDRs) of phone calls and SMS messages of one million Turk Telekom customers, indicates the broad activity and mobility patterns of refugees and citizens in Turkey for the year 1 January to 31 December 2017. Over 100 teams from around the globe applied to take part in the challenge, and 61 teams were granted access to the data.

This book describes the challenge, and presents selected and revised project reports on the five major themes: unemployment, health, education, social integration, and safety, respectively. These are complemented by additional invited chapters describing related projects from international governmental organizations, technological infrastructure, as well as ethical aspects. The last chapter includes policy recommendations, based on the lessons learned.

The book will serve as a guideline for creating innovative data-centered collaborations between industry, academia, government, and non-profit humanitarian agencies to deal with complex problems in refugee scenarios. It illustrates the possibilities of big data analytics in coping with refugee crises and humanitarian responses, by showcasing innovative approaches drawing on multiple data sources, information visualization, pattern analysis, and statistical analysis.It will also provide researchers and students working with mobility data with an excellent coverage across data science, economics, sociology, urban computing, education, migration studies, and more….(More)”.

#Kremlin: Using Hashtags to Analyze Russian Disinformation Strategy and Dissemination on Twitter

Curated on Posted on by Stefaan Verhulst

Paper by Sarah Oates, and John Gray: “Reports of Russian interference in U.S. elections have raised grave concerns about the spread of foreign disinformation on social media sites, but there is little detailed analysis that links traditional political communication theory to social media analytics. As a result, it is difficult for researchers and analysts to gauge the nature or level of the threat that is disseminated via social media. This paper leverages both social science and data science by using traditional content analysis and Twitter analytics to trace how key aspects of Russian strategic narratives were distributed via #skripal, #mh17, #Donetsk, and #russophobia in late 2018.

This work will define how key Russian international communicative goals are expressed through strategic narratives, describe how to find hashtags that reflect those narratives, and analyze user activity around the hashtags. This tests both how Twitter amplifies specific information goals of the Russians as well as the relative success (or failure) of particular hashtags to spread those messages effectively. This research uses Mentionmapp, a system co-developed by one of the authors (Gray) that employs network analytics and machine intelligence to identify the behavior of Twitter users as well as generate profiles of users via posting history and connections. This study demonstrates how political communication theory can be used to frame the study of social media; how to relate knowledge of Russian strategic priorities to labels on social media such as Twitter hashtags; and to test this approach by examining a set of Russian propaganda narratives as they are represented by hashtags. Our research finds that some Twitter users are consistently active across multiple Kremlin-linked hashtags, suggesting that knowledge of these hashtags is an important way to identify Russian propaganda online influencers. More broadly, we suggest that Twitter dichotomies such as bot/human or troll/citizen should be used with caution and analysis should instead address the nuances in Twitter use that reflect varying levels of engagement or even awareness in spreading foreign disinformation online….(More)”.