Your old tweets give away more location data than you think

Curated on Posted on by Stefaan Verhulst

Issie Lapowsky at Wired: “An international group of researchers has developed an algorithmic tool that uses Twitter to automatically predict exactly where you live in a matter of minutes, with more than 90 percent accuracy. It can also predict where you work, where you pray, and other information you might rather keep private, like, say, whether you’ve frequented a certain strip club or gone to rehab.

The tool, called LPAuditor (short for Location Privacy Auditor), exploits what the researchers call an “invasive policy” Twitter deployed after it introduced the ability to tag tweets with a location in 2009. For years, users who chose to geotag tweets with any location, even something as geographically broad as “New York City,” also automatically gave their precise GPS coordinates. Users wouldn’t see the coordinates displayed on Twitter. Nor would their followers. But the GPS information would still be included in the tweet’s metadata and accessible through Twitter’s API.

Twitter didn’t change this policy across its apps until April of 2015. Now, users must opt-in to share their precise location—and, according to a Twitter spokesperson, a very small percentage of people do. But the GPS data people shared before the update remains available through the API to this day.

The researchers developed LPAuditor to analyze those geotagged tweets and infer detailed information about people’s most sensitive locations. They outline this process in a new, peer-reviewed paper that will be presented at the Network and Distributed System Security Symposium next month. By analyzing clusters of coordinates, as well as timestamps on the tweets, LPAuditor was able to suss out where tens of thousands of people lived, worked, and spent their private time…(More)”.

High-performance medicine: the convergence of human and artificial intelligence

Curated on Posted on by Stefaan Verhulst

Eric Topol in Nature: “The use of artificial intelligence, and the deep-learning subtype in particular, has been enabled by the use of labeled big data, along with markedly enhanced computing power and cloud storage, across all sectors. In medicine, this is beginning to have an impact at three levels: for clinicians, predominantly via rapid, accurate image interpretation; for health systems, by improving workflow and the potential for reducing medical errors; and for patients, by enabling them to process their own data to promote health. The current limitations, including bias, privacy and security, and lack of transparency, along with the future directions of these applications will be discussed in this article. Over time, marked improvements in accuracy, productivity, and workflow will likely be actualized, but whether that will be used to improve the patient–doctor relationship or facilitate its erosion remains to be seen….(More)”.

Paying Users for Their Data Would Exacerbate Digital Inequality

Curated on Posted on by Stefaan Verhulst

Blog post by Eline Chivot: “Writing ever more complicated and intrusive regulations rules about data processing and data use has become the new fad in policymaking. Many are lending an ear to tempting yet ill-advised proposals to treat personal data as traditional finite resource. The latest example can be found in an article, A Blueprint for a Better Digital Society, by Glen Weyl, an economist at Microsoft Research, and Jaron Lanier, a computer scientist and writer. Not content with Internet users being able to access many online services like Bing and Twitter for free, they want online users to be paid in cash for the data they provide. To say that this proposal is flawed is an understatement. Its flawed for three main reasons: 1) consumers would lose significant shared value in exchange for minimal cash compensation; 2) higher incomes individuals would benefit at the expense of the poor; and 3) transaction costs would increase substantially, further reducing value for consumers and limiting opportunities for businesses to innovate with the data.

Weyl and Lanier’s argument is motivated by the belief that because Internet users are getting so many valuable services—like search, email, maps, and social networking—for free, they must be paying with their data. Therefore, they argue, if users are paying with their data, they should get something in return. Never mind that they do get something in return: valuable digital services that they do not pay for monetarily. But Weyl and Lanier say this is not enough, and consumers should get more.

While this idea may sound good on paper, in practice, it would be a disaster.

…Weyl and Lanier’s self-declared objective is to ensure digital dignity, but in practice this proposal would disrupt the equal treatment users receive from digital services today by valuing users based on their net worth. In this techno-socialist nirvana, to paraphrase Orwell, some pigs would be more equal than others. The French Data Protection Authority, CNIL, itself raised concerns about treating data as a commodity, warning that doing so would jeopardize society’s humanist values and fundamental rights which are, in essence, priceless.

To ensure “a better digital society,” companies should continue to be allowed to decide the best Internet business models based on what consumers demand. Data is neither cash nor a commodity, and pursuing policies based on this misconception will damage the digital economy and make the lives of digital consumers considerably worse….(More)”.

Innovations in satellite measurements for development

Curated on Posted on by Stefaan Verhulst

Ran Goldblatt, Trevor Monroe, Sarah Elizabeth Antos, Marco Hernandez at the World Bank Data Blog: “The desire of human beings to “think spatially” to understand how people and objects are organized in space has not changed much since Eratosthenes—the Greek astronomer best known as the “father of Geography”—first used the term “Geographika” around 250 BC. Centuries later, our understanding of economic geography is being propelled forward by new data and new capabilities to rapidly process, analyze and convert these vast data flows into meaningful and near real-time information.

The increasing availability of satellite data has transformed how we use remote sensing analytics to understand, monitor and achieve the 2030 Sustainable Development Goals. As satellite data becomes ever more accessible and frequent, it is now possible not only to better understand how the Earth is changing, but also to utilize these insights to improve decision making, guide policy, deliver services, and promote better-informed governance. Satellites capture many of the physical, economic and social characteristics of Earth, providing a unique asset for developing countries, where reliable socio-economic and demographic data is often not consistently available. Analysis of satellite data was once relegated to researchers with access to costly data or to “super computers”. Today, the increased availability of “free” satellite data, combined with powerful cloud computing and open source analytical tools have democratized data innovation, enabling local governments and agencies to use satellite data to improve sector diagnostics, development indicators, program monitoring and service delivery.

Drivers of innovation in satellite measurements

  • Big (geo)data – Satellites in Global Development are improving every day, creating new opportunities for impact in development. They capture millions of images from Earth in different spatial, spectral and temporal resolutions, generating data in ever increasing volume, variety and velocity.
  • Open Source –Open source annotated datasets, the World Bank’s Open Data, and other publicly available resources allow to process and document the data (e.g. Cumulus, label maker) and perform machine learning analysis using common programming languages such as R or Python.
  • Crowd – crowdsource platforms like MTurk, Figure-eight and Tomnod are used to collect and enhance inputs (reference data) to train machines to identify automatically specific objects and land cover on Earth.
  • High Quality Ground Truth –Robust algorithms that analyze the entire planet require diverse training data, and traditional development Microdata for use in machine learning training, validation and calibration, for example, to map urbanization processes.
  • Cloud – cloud computing and data storage capabilities within platforms like AWS, Azure and Google Earth Engine provide scalable solutions for storage, management and parallel processing of large volumes of data.

…As petabytes of geo data are being collected, novel methods are developed to convert these data into meaningful information about the nature and pace of change on Earth, for example, the formation of urban landscapes and human settlements, the creation of transportation networks that connect cities or the conversion of natural forests into productive agricultural land. New possibilities emerge for harnessing this data for a better understanding about our changing world….(More)”.

Digital rights as a security objective: New gateways for attacks

Curated on Posted on by Stefaan Verhulst

Yannic Blaschke at EDRI: “Violations of human rights online, most notably the right to data protection, can pose a real threat to electoral security and societal polarisation. In this series of blogposts, we’ll explain how and why digital rights must be treated as a security objective instead. The second part of the series explains how encroaching on digital rights could create new gateways for attacks against our security.

In the first part of this series, we analysed the failure of the Council of the European Union to connect the obvious dots between ePrivacy and disinformation online, leaving open a security vulnerability through a lack of protection of citizens. However, a failure to act is not the only front on which the EU is potentially weakening our security on- and offline: on the contrary, some of the EU’s more actively pursued digital policies could have unintended, yet serious consequences in the future. Nowhere is this trend more visible than in the recent trust in filtering algorithms, which seem to be the new “censorship machine” that is proposed as a solution for almost everything, from copyright infringements to terrorist content online.

Article 13 of the Copyright Directive proposal and the Terrorist Content Regulation proposal are two examples of the attempt to regulate the online world via algorithms. While having different motivations, both share the logic of outsourcing accountability and enforcement of public rules to private entities who will be the ones deciding about the availability of speech online. They, explicitly or implicitly, advocate for the introduction of technologies that detect and remove certain types of content: upload filters. They empower internet companies to decide which content will stay online, based on their terms of service (and not law). In a nutshell, public institutions are encouraging Google, Facebook and other platform giants to become the judge and the police of the internet. In turn, they undermine the presumption that it should be democratically legitimise states, not private entities, who are tasked with the heavy burden of balancing the right to freedom of expression.

Even more chilling is the outlook of upload filters creating new entry points for forces that seek to influence societal debates in their favour. If algorithms will be the judges of what can or cannot be published, they could become the target of the next wave of election interference campaigns, with attackers instigating them to take down critical or liberal voices to influence debates on the internet. Despite continuous warnings about the misuse of personal data on Facebook, it only took us a few years to arrive at the point of Cambridge Analytica. How long will it take us to arrive at a similar point of election interference through upload filters in online platforms?

If we let this pre-emptive and extra-judicial censorship happen, it would likely result in severe detriments to the freedom of speech and right to information of European citizens, and the free flow of information would, in consequence, be stifled. The societal effects of this could be further aggravated by the introduction of a press publishers right (Article 11 of the Copyright Directive) that is vividly opposed by the academic world, as it will concentrate the power over what appears in the news in ever fewer hands. Especially in Member States where media plurality and independence of bigger outlets from state authorities are no longer guaranteed, a decline in societal resilience to authoritarian tendencies is unfortunately easy to imagine.

We have to be very clear about what machines are good at and what they are bad at: Algorithms are incredibly well suited to detect patterns and trends, but cannot and will not be able perform the delicate act of balancing our rights and freedoms in accordance with the law any time soon….(More)”

The promises — and challenges — of data collaboratives for the SDGs

Curated on Posted on by Stefaan Verhulst

Paula Hidalgo-Sanchis and Stefaan G. Verhulst at Devex: “As the road to achieving the Sustainable Development Goals becomes more complex and challenging, policymakers around the world need both new solutions and new ways to become more innovative. This includes better policy and program design based on evidence to solve problems at scale. The use of big data — the vast majority of which is collected, processed, and analyzed by the private sector — is key.

In the past few months, we at UN Global Pulse and The GovLab have sought to understand pathways to make policymaking more evidence-based and data-driven with the use of big data. Working in parallel at both local and global scale, we have conducted extensive desk research, held a series of workshops, and conducted in-depth conversations and interviews with key stakeholders, including government, civil society, and private sector representatives.

Our work is driven by a recognition of the potential of use of privately processed data through data collaboratives — a new form of public-private partnership in which government, private industry, and civil society work together to release previously siloed data, making it available to address the challenges of our era.

Research suggests that data collaboratives offer tremendous potential when implemented strategically under the appropriate policy and ethical frameworks. Nonetheless, this remains a nascent field, and we have summarized some of the barriers that continue to confront data collaboratives, with an eye toward ultimately proposing solutions to make them more effective, scalable, sustainable, and responsible.

Here are seven challenges…(More)”.

Blockchain’s Occam problem

Curated on Posted on by Stefaan Verhulst

Report by Matt Higginson, Marie-Claude Nadeau, and Kausik Rajgopal: “Blockchain has yet to become the game-changer some expected. A key to finding the value is to apply the technology only when it is the simplest solution available.

Blockchain over recent years has been extolled as a revolution in business technology. In the nine years since its launch, companies, regulators, and financial technologists have spent countless hours exploring its potential. The resulting innovations have started to reshape business processes, particularly in accounting and transactions.

Amid intense experimentation, industries from financial services to healthcare and the arts have identified more than 100 blockchain use cases. These range from new land registries, to KYC applications and smart contracts that enable actions from product processing to share trading. The most impressive results have seen blockchains used to store information, cut out intermediaries, and enable greater coordination between companies, for example in relation to data standards….

There is a clear sense that blockchain is a potential game-changer. However, there are also emerging doubts. A particular concern, given the amount of money and time spent, is that little of substance has been achieved. Of the many use cases, a large number are still at the idea stage, while others are in development but with no output. The bottom line is that despite billions of dollars of investment, and nearly as many headlines, evidence for a practical scalable use for blockchain is thin on the ground.

Infant technology

From an economic theory perspective, the stuttering blockchain development path is not entirely surprising. It is an infant technology that is relatively unstable, expensive, and complex. It is also unregulated and selectively distrusted. Classic lifecycle theory suggests the evolution of any industry or product can be divided into four stages: pioneering, growth, maturity, and decline (exhibit). Stage 1 is when the industry is getting started, or a particular product is brought to market. This is ahead of proven demand and often before the technology has been fully tested. Sales tend to be low and return on investment is negative. Stage 2 is when demand begins to accelerate, the market expands and the industry or product “takes off.”

Blockchain is struggling to emerge from the pioneering stage.
Exhibit

Across its many applications, blockchain arguably remains stuck at stage 1 in the lifecycle (with a few exceptions). The vast majority of proofs of concept (POCs) are in pioneering mode (or being wound up) and many projects have failed to get to Series C funding rounds.

One reason for the lack of progress is the emergence of competing technologies. In payments, for example, it makes sense that a shared ledger could replace the current highly intermediated system. However, blockchains are not the only game in town. Numerous fintechs are disrupting the value chain. Of nearly $12 billion invested in US fintechs last year, 60 percent was focused on payments and lending. SWIFT’s global payments innovation initiative (GPI), meanwhile, is addressing initial pain points through higher transaction speeds and increased transparency, building on bank collaboration….(More)” (See also: Blockchange)

Smart cities could be lousy to live in if you have a disability

Curated on Posted on by Stefaan Verhulst

Elizabeth Woyke in MIT Technology Review: “People with disabilities affecting mobility, vision, hearing, and cognitive function often move to cities to take advantage of their comprehensive transit systems and social services. But US law doesn’t specify how municipalities should design and implement digital services for disabled people. As a result, cities sometimes adopt new technologies that can end up causing, rather than resolving, problems of accessibility.

Nowhere was this more evident than with New York City’s LinkNYC kiosks, which were installed on sidewalks in 2016 without including instructions in Braille or audible form. Shortly after they went in, the American Federation for the Blind sued the city. The suit was settled in 2017 and the kiosks have been updated, but Pineda says touch screens in general are still not fully accessible to people with disabilities.

Also problematic: the social-media-based apps that some municipal governments have started using to solicit feedback from residents. Blind and low-vision people typically can’t use the apps, and people over 65 are less likely to, says James Thurston, a vice president at the nonprofit G3ict, which promotes accessible information and communication technologies. “Cities may think they’re getting data from all their residents, but if those apps aren’t accessible, they’re leaving out the voices of large chunks of their population,” he says….

Even for city officials who have these issues on their minds, knowing where to begin can be difficult. Smart Cities for All, an initiative led by Thurston and Pineda, aims to help by providing free, downloadable tools that cities can use to analyze their technology and find more accessible options. One is a database of hundreds of pre-vetted products and services. Among the entries are Cyclomedia, which uses lidar data to determine when city sidewalks need maintenance, and ZenCity, a data analytics platform that uses AI to gauge what people are saying about a city’s level of accessibility. 

This month, the group will kick off a project working with officials in Chicago to grade the city on how well it supports people with disabilities. One key part of the project will be ensuring the accessibility of a new 311 phone system being introduced as a general portal to city services. The group has plans to expand to several other US cities this year, but its ultimate aim is to turn the work into a global movement. It’s met with governments in India and Brazil as well as Sidewalk Labs, the Alphabet subsidiary that is developing a smart neighborhood in Toronto….(More)”.

A Study of the Implications of Advanced Digital Technologies (Including AI Systems) for the Concept of Responsibility Within a Human Rights Framework

Curated on Posted on by Stefaan Verhulst

Report by Karen Yeung: “This study was commissioned by the Council of Europe’s Committee of experts on human rights dimensions of automated data processing and different forms of artificial intelligence (MSI-AUT). It was prompted by concerns about the potential adverse consequences of advanced digital technologies (including artificial intelligence (‘AI’)), particularly their impact on the enjoyment of human rights and fundamental freedoms. This draft report seeks to examine the implications of these technologies for the concept of responsibility, and this includes investigating where responsibility should lie for their adverse consequences. In so doing, it seeks to understand (a) how human rights and fundamental freedoms protected under the ECHR may be adversely affected by the development of AI technologies and (b) how responsibility for those risks and consequences should be allocated. 

Its methodological approach is interdisciplinary, drawing on concepts and academic scholarship from the humanities, the social sciences and, to a more limited extent, from computer science. It concludes that, if we are to take human rights seriously in a hyperconnected digital age, we cannot allow the power of our advanced digital technologies and systems, and those who develop and implement them, to be accrued and exercised without responsibility. Nations committed to protecting human rights must therefore ensure that those who wield and derive benefits from developing and deploying these technologies are held responsible for their risks and consequences. This includes obligations to ensure that there are effective and legitimate mechanisms that will operate to prevent and forestall violations to human rights which these technologies may threaten, and to attend to the health of the larger collective and shared socio-technical environment in which human rights and the rule of law are anchored….(More)”.

Societal costs and benefits of high-value open government data: a case study in the Netherlands

Curated on Posted on by Stefaan Verhulst

Paper by F.M. Welle Donker and B. van Loenen: “Much research has emphasised the benefits of open government data, and especially high-value data. The G8 Open Data Charter defines high-value data as data that improve democracy and encourage the innovative reuse of the particular data. Thus, governments worldwide invest resources to identify potential high-value datasets and to publish these data as open data. However, while the benefits of open data are well researched, the costs of publishing data as open data are less researched. This research examines the relationship between the costs of making data suitable for publication as (linked) open data and the societal benefits thereof. A case study of five high-value datasets was carried out in the Netherlands to provide a societal cost-benefit analysis of open high-value data. Different options were investigated, ranging from not publishing the dataset at all to publishing the dataset as linked open data.

In general, it can be concluded that the societal benefits of (linked) open data are higher than the costs. The case studies show that there are differences between the datasets. In many cases, costs for open data are an integral part of general data management costs and hardly lead to additional costs. In certain cases, however, the costs to anonymize /aggregate the data are high compared to the potential value of an open data version of the dataset. Although, for these datasets, this leads to a less favourable relationship between costs and benefits, the societal benefits would still be higher than without an open data version….(More)”.