The Future of Democracy in Europe: Technology and the Evolution of Representation

Curated on Posted on by Stefaan Verhulst

Report by Chatham House: “There is a widespread sense that liberal democracy is in crisis, but little consensus exists on the specific nature and causes of the crisis. In particular, there are three prisms through which the crisis is usually seen: the rise of ‘populism’, ‘democratic deconsolidation’, and a ‘hollowing out’ of democracy. Each reflects normative assumptions about democracy.

The exact role of digital technology in the crisis is disputed. Despite the widely held perception that social media is undermining democracy, the evidence for this is limited. Over the longer term, the further development of digital technology could undermine the fundamental preconditions for democracy – though the pace and breadth of technological change make predictions about its future impact difficult.

Democracy functions in different ways in different European countries, with political systems on the continent ranging from ‘majoritarian democracies’ such as the UK to ‘consensual democracies’ such as Belgium and Switzerland. However, no type seems to be immune from the crisis. The political systems of EU member states also interact in diverse ways with the EU’s own structure, which is problematic for representative democracy as conventionally understood, but difficult to reform.

Political parties, central to the model of representative democracy that emerged in the late 18th century, have long seemed to be in decline. Recently there have been some signs of a reversal of this trend, with the emergence of parties that have used digital technology in innovative ways to reconnect with citizens. Traditional parties can learn from these new ‘digital parties’.

Recent years have also seen a proliferation of experiments in direct and deliberative democracy. There is a need for more experimentation in these alternative forms of democracy, and for further evaluation of how they can be integrated into the existing institutions and processes of representative democracy at the local, regional, national and EU levels.

We should not think of democracy in a static way – that is, as a system that can be perfected once and for all and then simply maintained and defended against threats. Democracy has continually evolved and now needs to evolve further. The solution to the crisis will not be to attempt to limit democracy in response to pressure from ‘populism’ but to deepen it further as part of a ‘democratization of democracy’….(More)”.

Car Data Facts

Curated on Posted on by Stefaan Verhulst

About: “Welcome to CarDataFacts.eu! This website provides a fact-based overview on everything related to the sharing of vehicle-generated data with third parties. Through a series of educational infographics, this website answers the most common questions about access to car data in a clear and simple way.

CarDataFacts.eu also addresses consumer concerns about sharing data in a safe and a secure way, as well as explaining some of the complex and technical terminology surrounding the debate.

CarDataFacts.eu is brought to you by ACEA, the European Automobile Manufacturers’ Association, which represents the 15 Europe-based car, van, truck and bus makers….(More)”.

Automation in Moderation

Curated on Posted on by Stefaan Verhulst

Article by Hannah Bloch-Wehba: “This Article assesses recent efforts to compel or encourage online platforms to use automated means to prevent the dissemination of unlawful online content before it is ever seen or distributed. As lawmakers in Europe and around the world closely scrutinize platforms’ “content moderation” practices, automation and artificial intelligence appear increasingly attractive options for ridding the Internet of many kinds of harmful online content, including defamation, copyright infringement, and terrorist speech. Proponents of these initiatives suggest that requiring platforms to screen user content using automation will promote healthier online discourse and will aid efforts to limit Big Tech’s power.

In fact, however, the regulations that incentivize platforms to use automation in content moderation come with unappreciated costs for civil liberties and unexpected benefits for platforms. The new automation techniques exacerbate existing risks to free speech and user privacy and create ripe new sources of information for surveillance, aggravating threats to free expression, associational rights, religious freedoms, and equality. Automation also worsens transparency and accountability deficits. Far from curtailing private power, the new regulations endorse and expand platform authority to police online speech, with little in the way of oversight and few countervailing checks. New regulations of online intermediaries should therefore incorporate checks on the use of automation to avoid exacerbating these dynamics. Carefully drawn transparency obligations, algorithmic accountability mechanisms, and procedural safeguards can help to ameliorate the effects of these regulations on users and competition…(More)”.

Many Tech Experts Say Digital Disruption Will Hurt Democracy

Curated on Posted on by Stefaan Verhulst

Lee Rainie and Janna Anderson at Pew Research Center: “The years of almost unfettered enthusiasm about the benefits of the internet have been followed by a period of techlash as users worry about the actors who exploit the speed, reach and complexity of the internet for harmful purposes. Over the past four years – a time of the Brexit decision in the United Kingdom, the American presidential election and a variety of other elections – the digital disruption of democracy has been a leading concern.

The hunt for remedies is at an early stage. Resistance to American-based big tech firms is increasingly evident, and some tech pioneers have joined the chorus. Governments are actively investigating technology firms, and some tech firms themselves are requesting government regulation. Additionally, nonprofit organizations and foundations are directing resources toward finding the best strategies for coping with the harmful effects of disruption. For example, the Knight Foundation announced in 2019 that it is awarding $50 million in grants to encourage the development of a new field of research centered on technology’s impact on democracy.

In light of this furor, Pew Research Center and Elon University’s Imagining the Internet Center canvassed technology experts in the summer of 2019 to gain their insights about the potential future effects of people’s use of technology on democracy….

The main themes found in an analysis of the experts’ comments are outlined in the next two tables….(More)”.

Accelerating AI with synthetic data

Curated on Posted on by Stefaan Verhulst

Essay by Khaled El Emam: “The application of artificial intelligence and machine learning to solve today’s problems requires access to large amounts of data. One of the key obstacles faced by analysts is access to this data (for example, these issues were reflected in reports from the General Accountability Office and the McKinsey Institute).

Synthetic data can help solve this data problem in a privacy preserving manner.

What is synthetic data ?

Data synthesis is an emerging privacy-enhancing technology that can enable access to realistic data, which is information that may be synthetic, but has the properties of an original dataset. It also simultaneously ensures that such information can be used and disclosed with reduced obligations under contemporary privacy statutes. Synthetic data retains the statistical properties of the original data. Therefore, there are an increasing number of use cases where it would serve as a proxy for real data.

Synthetic data is created by taking an original (real) dataset and then building a model to characterize the distributions and relationships in that data — this is called the “synthesizer.” The synthesizer is typically an artificial neural network or other machine learning technique that learns these (original) data characteristics. Once that model is created, it can be used to generate synthetic data. The data is generated from the model and does not have a 1:1 mapping to real data, meaning that the likelihood of mapping the synthetic records to real individuals would be very small — it is not considered personal information.

Many different types of data can be synthesized, including images, video, audio, text and structured data. The main focus in this article is on the synthesis of structured data.

Even though data can be generated in this manner, that does not mean it cannot be personal information. If the synthesizer is overfit to real data, then the generated data will replicate the original real data. Therefore, the synthesizer has to be constructed in a manner to avoid such overfitting. A formal privacy assurance should also be performed on the synthesized data to validate that there is a weak mapping between synthetic records to individuals….(More)”.

The Economic Impact of Open Data: Opportunities for value creation in Europe

Curated on Posted on by Stefaan Verhulst

Press Release: “The European Data Portal publishes its study “The Economic Impact of Open Data: Opportunities for value creation in Europe”. It researches the value created by open data in Europe. It is the second study by the European Data Portal, following the 2015 report. The open data market size is estimated at €184 billion and forecast to reach between €199.51 and €334.21 billion in 2025. The report additionally considers how this market size is distributed along different sectors and how many people are employed due to open data. The efficiency gains from open data, such as potential lives saved, time saved, environmental benefits, and improvement of language services, as well as associated potential costs savings are explored and quantified where possible. Finally, the report also considers examples and insights from open data re-use in organisations. The key findings of the report are summarised below:

  1. The specification and implementation of high-value datasets as part of the new Open Data Directive is a promising opportunity to address quality & quantity demands of open data.
  2. Addressing quality & quantity demands is important, yet not enough to reach the full potential of open data.
  3. Open data re-users have to be aware and capable of understanding and leveraging the potential.
  4. Open data value creation is part of the wider challenge of skill and process transformation: a lengthy process whose change and impact are not always easy to observe and measure.
  5. Sector-specific initiatives and collaboration in and across private and public sector foster value creation.
  6. Combining open data with personal, shared, or crowdsourced data is vital for the realisation of further growth of the open data market.
  7. For different challenges, we must explore and improve multiple approaches of data re-use that are ethical, sustainable, and fit-for-purpose….(More)”.

Mapping Wikipedia

Curated on Posted on by Stefaan Verhulst

Michael Mandiberg at The Atlantic: “Wikipedia matters. In a time of extreme political polarization, algorithmically enforced filter bubbles, and fact patterns dismissed as fake news, Wikipedia has become one of the few places where we can meet to write a shared reality. We treat it like a utility, and the U.S. and U.K. trust it about as much as the news.

But we know very little about who is writing the world’s encyclopedia. We do know that just because anyone can edit, doesn’t mean that everyone does: The site’s editors are disproportionately cis white men from the global North. We also know that, as with most of the internet, a small number of the editors do a large amount of the editing. But that’s basically it: In the interest of improving retention, the Wikimedia Foundation’s own research focuses on the motivations of people who do edit, not on those who don’t. The media, meanwhile, frequently focus on Wikipedia’s personality stories, even when covering the bigger questions. And Wikipedia’s own culture pushes back against granular data harvesting: The Wikimedia Foundation’s strong data-privacy rules guarantee users’ anonymity and limit the modes and duration of their own use of editor data.

But as part of my research in producing Print Wikipedia, I discovered a data set that can offer an entry point into the geography of Wikipedia’s contributors. Every time anyone edits Wikipedia, the software records the text added or removed, the time of the edit, and the username of the editor. (This edit history is part of Wikipedia’s ethos of radical transparency: Everyone is anonymous, and you can see what everyone is doing.) When an editor isn’t logged in with a username, the software records that user’s IP address. I parsed all of the 884 million edits to English Wikipedia to collect and geolocate the 43 million IP addresses that have edited English Wikipedia. I also counted 8.6 million username editors who have made at least one edit to an article.

The result is a set of maps that offer, for the first time, insight into where the millions of volunteer editors who build and maintain English Wikipedia’s 5 million pages are—and, maybe more important, where they aren’t….

Like the Enlightenment itself, the modern encyclopedia has a history entwined with colonialism. Encyclopédie aimed to collect and disseminate all the world’s knowledge—but in the end, it could not escape the biases of its colonial context. Likewise, Napoleon’s Description de l’Égypte augmented an imperial military campaign with a purportedly objective study of the nation, which was itself an additional form of conquest. If Wikipedia wants to break from the past and truly live up to its goal to compile the sum of all human knowledge, it requires the whole world’s participation….(More)”.

Identifying Urban Areas by Combining Human Judgment and Machine Learning: An Application to India

Curated on Posted on by Stefaan Verhulst

Paper by Virgilio Galdo, Yue Li and Martin Rama: “This paper proposes a methodology for identifying urban areas that combines subjective assessments with machine learning, and applies it to India, a country where several studies see the official urbanization rate as an under-estimate. For a representative sample of cities, towns and villages, as administratively defined, human judgment of Google images is used to determine whether they are urban or rural in practice. Judgments are collected across four groups of assessors, differing in their familiarity with India and with urban issues, following two different protocols. The judgment-based classification is then combined with data from the population census and from satellite imagery to predict the urban status of the sample.

The Logit model, and LASSO and random forests methods, are applied. These approaches are then used to decide whether each of the out-of-sample administrative units in India is urban or rural in practice. The analysis does not find that India is substantially more urban than officially claimed. However, there are important differences at more disaggregated levels, with ?other towns? and ?census towns? being more rural, and some southern states more urban, than is officially claimed. The consistency of human judgment across assessors and protocols, the easy availability of crowd-sourcing, and the stability of predictions across approaches, suggest that the proposed methodology is a promising avenue for studying urban issues….(More)”.

We All Wear Tinfoil Hats Now

Curated on Posted on by Stefaan Verhulst

Article by Geoff Shullenberger on “How fears of mind control went from paranoid delusion to conventional wisdom”: “In early 2017, after the double shock of Brexit and the election of Donald Trump, the British data-mining firm Cambridge Analytica gained sudden notoriety. The previously little-known company, reporters claimed, had used behavioral influencing techniques to turn out social media users to vote in both elections. By its own account, Cambridge Analytica had worked with both campaigns to produce customized propaganda for targeting individuals on Facebook likely to be swept up in the tide of anti-immigrant populism. Its methods, some news sources suggested, might have sent enough previously disengaged voters to the polls to have tipped the scales in favor of the surprise victors. To a certain segment of the public, this story seemed to answer the question raised by both upsets: How was it possible that the seemingly solid establishment consensus had been rejected? What’s more, the explanation confirmed everything that seemed creepy about the Internet, evoking a sci-fi vision of social media users turned into an army of political zombies, mobilized through subliminal manipulation.

Cambridge Analytica’s violations of Facebook users’ privacy have made it an enduring symbol of the dark side of social media. However, the more dramatic claims about the extent of the company’s political impact collapse under closer scrutiny, mainly because its much-hyped “psychographic targeting” methods probably don’t work. As former Facebook product manager Antonio García Martínez noted in a 2018 Wired article, “the public, with no small help from the media sniffing a great story, is ready to believe in the supernatural powers of a mostly unproven targeting strategy,” but “most ad insiders express skepticism about Cambridge Analytica’s claims of having influenced the election, and stress the real-world difficulty of changing anyone’s mind about anything with mere Facebook ads, least of all deeply ingrained political views.” According to García, the entire affair merely confirms a well-established truth: “In the ads world, just because a product doesn’t work doesn’t mean you can’t sell it….(More)”.

Experts say privately held data available in the European Union should be used better and more

Curated on Posted on by Stefaan Verhulst

European Commission: “Data can solve problems from traffic jams to disaster relief, but European countries are not yet using this data to its full potential, experts say in a report released today. More secure and regular data sharing across the EU could help public administrations use private sector data for the public good.

In order to increase Business-to-Government (B2G) data sharing, the experts advise to make data sharing in the EU easier by taking policy, legal and investment measures in three main areas:

  1. Governance of B2G data sharing across the EU: such as putting in place national governance structures, setting up a recognised function (‘data stewards’) in public and private organisations, and exploring the creation of a cross-EU regulatory framework.
  2. Transparency, citizen engagement and ethics: such as making B2G data sharing more citizen-centric, developing ethical guidelines, and investing in training and education.
  3. Operational models, structures and technical tools: such as creating incentives for companies to share data, carrying out studies on the benefits of B2G data sharing, and providing support to develop the technical infrastructure through the Horizon Europe and Digital Europe programmes.

They also revised the principles on private sector data sharing in B2G contexts and included new principles on accountability and on fair and ethical data use, which should guide B2G data sharing for the public interest. Examples of successful B2G data sharing partnerships in the EU include an open forest data system in Finland to help manage the ecosystem, mapping of EU fishing activities using ship tracking data, and genome sequencing data of breast cancer patients to identify new personalised treatments. …

The High-Level Expert Group on Business-to-Government Data Sharing was set up in autumn 2018 and includes members from a broad range of interests and sectors. The recommendations presented today in its final report feed into the European strategy for data and can be used as input for other possible future Commission initiatives on Business-to-Government data sharing….(More)”.