Computational Propaganda and Political Big Data: Moving Toward a More Critical Research Agenda

Curated on Posted on by Stefaan Verhulst

Gillian Bolsover and Philip Howard in the Journal Big Data: “Computational propaganda has recently exploded into public consciousness. The U.S. presidential campaign of 2016 was marred by evidence, which continues to emerge, of targeted political propaganda and the use of bots to distribute political messages on social media. This computational propaganda is both a social and technical phenomenon. Technical knowledge is necessary to work with the massive databases used for audience targeting; it is necessary to create the bots and algorithms that distribute propaganda; it is necessary to monitor and evaluate the results of these efforts in agile campaigning. Thus, a technical knowledge comparable to those who create and distribute this propaganda is necessary to investigate the phenomenon.

However, viewing computational propaganda only from a technical perspective—as a set of variables, models, codes, and algorithms—plays into the hands of those who create it, the platforms that serve it, and the firms that profit from it. The very act of making something technical and impartial makes it seem inevitable and unbiased. This undermines the opportunities to argue for change in the social value and meaning of this content and the structures in which it exists. Big-data research is necessary to understand the socio-technical issue of computational propaganda and the influence of technology in politics. However, big data researchers must maintain a critical stance toward the data being used and analyzed so as to ensure that we are critiquing as we go about describing, predicting, or recommending changes. If research studies of computational propaganda and political big data do not engage with the forms of power and knowledge that produce it, then the very possibility for improving the role of social-media platforms in public life evaporates.

Definitionally, computational propaganda has two important parts: the technical and the social. Focusing on the technical, Woolley and Howard define computational propaganda as the assemblage of social-media platforms, autonomous agents, and big data tasked with the manipulation of public opinion. In contrast, the social definition of computational propaganda derives from the definition of propaganda—communications that deliberately misrepresent symbols, appealing to emotions and prejudices and bypassing rational thought, to achieve a specific goal of its creators—with computational propaganda understood as propaganda created or disseminated using computational (technical) means…(More) (Full Text HTML; Full Text PDF)

Behind the Screen: the Syrian Virtual Resistance

Curated on Posted on by Stefaan Verhulst

Billie Jeanne Brownlee at Cyber Orient: “Six years have gone by since the political upheaval that swept through many Middle East and North African (MENA) countries begun. Syria was caught in the grip of this revolutionary moment, one that drove the country from a peaceful popular mobilisation to a deadly fratricide civil war with no apparent way out.

This paper provides an alternative approach to the study of the root causes of the Syrian uprising by examining the impact that the development of new media had in reconstructing forms of collective action and social mobilisation in pre-revolutionary Syria.

By providing evidence of a number of significant initiatives, campaigns and acts of contentious politics that occurred between 2000 and 2011, this paper shows how, prior to 2011, scholarly work on Syria has not given sufficient theoretical and empirical consideration to the development of expressions of dissent and resilience of its cyberspace and to the informal and hybrid civic engagement they produced….(More)”.

Research reveals de-identified patient data can be re-identified

Curated on Posted on by Stefaan Verhulst

Vanessa Teague, Chris Culnane and Ben Rubinstein in PhysOrg: “In August 2016, Australia’s federal Department of Health published medical billing records of about 2.9 million Australians online. These records came from the Medicare Benefits Scheme (MBS) and the Pharmaceutical Benefits Scheme (PBS) containing 1 billion lines of historical health data from the records of around 10 per cent of the population.

These longitudinal records were de-identified, a process intended to prevent a person’s identity from being connected with information, and were made public on the government’s open data website as part of its policy on accessible public …

We found that patients can be re-identified, without decryption, through a process of linking the unencrypted parts of the  with known information about the individual.

Our findings replicate those of similar studies of other de-identified datasets:

  • A few mundane facts taken together often suffice to isolate an individual.
  • Some patients can be identified by name from publicly available information.
  • Decreasing the precision of the data, or perturbing it statistically, makes re-identification gradually harder at a substantial cost to utility.

The first step is examining a patient’s uniqueness according to medical procedures such as childbirth. Some individuals are unique given public information, and many patients are unique given a few basic facts, such as year of birth or the date a baby was delivered….

The second step is examining uniqueness according to the characteristics of commercial datasets we know of but cannot access directly. There are high uniqueness rates that would allow linking with a commercial pharmaceutical dataset, and with the billing data available to a bank. This means that ordinary people, not just the prominent ones, may be easily re-identifiable by their bank or insurance company…

These de-identification methods were bound to fail, because they were trying to achieve two inconsistent aims: the protection of individual privacy and publication of detailed individual records. De-identification is very unlikely to work for other rich datasets in the government’s care, like census data, tax records, mental health records, penal information and Centrelink data.

While the ambition of making more data more easily available to facilitate research, innovation and sound public policy is a good one, there is an important technical and procedural problem to solve: there is no good solution for publishing sensitive complex individual records that protects privacy without substantially degrading the usefulness of the data.

Some data can be safely published online, such as information about government, aggregations of large collections of material, or data that is differentially private. For sensitive, complex data about individuals, a much more controlled release in a secure research environment is a better solution. The Productivity Commission recommends a “trusted user” model, and techniques like dynamic consent also give patients greater control and visibility over their personal information….(More).

The Annual Review of Social Partnerships

Curated on Posted on by Stefaan Verhulst

(Open access) book edited by May Seitanidi and Verena Bitzer: “…written for and by cross-sector social partnership (CSSP) academics and practitioners focusing on nonprofit, business, and public sectors, who view collaboration as key to solving social problems such as climate change, economic inequality, poverty, or biodiversity loss and environmental degradation. Published by an independent group of academics and practitioners since 2006, the ARSP bridges academic theory and practice with ideas about promoting the social good, covering a wide range of subjects and geographies surrounding the interactions between nonprofit, business, and public sectors. Its aim is to inform, to share, to inspire, to educate, and to train. Building a global community of experts on CSSPs, be they from academic or practice, is the inherent motivation of the ARSP. The ARSP offers new directions for research, presents funded research projects, and provides published papers in a compilation, allowing researchers to familiarize themselves with the latest work in this field. The ARSP also captures and presents insights on partnerships from practitioners, enabling its readership to learn from the hands-on experiences and observations of those who work with and for partnerships….(More)”. Issues of the ARSP can be downloaded here.

Accountability of AI Under the Law: The Role of Explanation

Curated on Posted on by Stefaan Verhulst

Paper by Finale Doshi-Velez and Mason Kortz: “The ubiquity of systems using artificial intelligence or “AI” has brought increasing attention to how those systems should be regulated. The choice of how to regulate AI systems will require care. AI systems have the potential to synthesize large amounts of data, allowing for greater levels of personalization and precision than ever before—applications range from clinical decision support to autonomous driving and predictive policing. That said, our AIs continue to lag in common sense reasoning [McCarthy, 1960], and thus there exist legitimate concerns about the intentional and unintentional negative consequences of AI systems [Bostrom, 2003, Amodei et al., 2016, Sculley et al., 2014]. How can we take advantage of what AI systems have to offer, while also holding them accountable?

In this work, we focus on one tool: explanation. Questions about a legal right to explanation from AI systems was recently debated in the EU General Data Protection Regulation [Goodman and Flaxman, 2016, Wachter et al., 2017a], and thus thinking carefully about when and how explanation from AI systems might improve accountability is timely. Good choices about when to demand explanation can help prevent negative consequences from AI systems, while poor choices may not only fail to hold AI systems accountable but also hamper the development of much-needed beneficial AI systems.

Below, we briefly review current societal, moral, and legal norms around explanation, and then focus on the different contexts under which explanation is currently required under the law. We find that there exists great variation around when explanation is demanded, but there also exist important consistencies: when demanding explanation from humans, what we typically want to know is whether and how certain input factors affected the final decision or outcome.

These consistencies allow us to list the technical considerations that must be considered if we desired AI systems that could provide kinds of explanations that are currently required of humans under the law. Contrary to popular wisdom of AI systems as indecipherable black boxes, we find that this level of explanation should generally be technically feasible but may sometimes be practically onerous—there are certain aspects of explanation that may be simple for humans to provide but challenging for AI systems, and vice versa. As an interdisciplinary team of legal scholars, computer scientists, and cognitive scientists, we recommend that for the present, AI systems can and should be held to a similar standard of explanation as humans currently are; in the future we may wish to hold an AI to a different standard….(More)”

The Wikipedia competitor that’s harnessing blockchain for epistemological supremacy

Curated on Posted on by Stefaan Verhulst

Peter Rubin at Wired: “At the time of this writing, the opening sentence of Larry Sanger’s Everipedia entry is pretty close to his Wikipedia entry. It describes him as “an American Internet project developer … best known as co-founder of Wikipedia.” By the time you read this, however, it may well mention a new, more salient fact—that Sanger recently became the Chief Information Officer of Everipedia itself, a site that seeks to become a better version of the online encyclopedia than the one he founded back in 2001. To do that, Sanger’s new employer is trying something that no other player in the space has done: moving to a blockchain.

Oh, blockchain, that decentralized “global ledger” that provides the framework for cryptocurrencies like Bitcoin (as well as a thousand explainer videos, and seemingly a thousand startups’ business plans). Blockchain already stands to make medical patient data easier to move and improve food safety; now, Everipedia’s founders hope, it will allow for a more powerful, accountable encyclopedia.

Here’s how it’ll work. Everipedia already uses a points system where creating articles and approved edits amasses “IQ.” In January, when the site moves over to a blockchain, Everipedia will convert IQ scores to a token-based currency, giving all existing editors an allotment proportionate to their IQ—and giving them a real, financial stake in Everipedia. From then on, creating and curating articles will allow users to earn tokens, which act as virtual shares of the platform. To prevent bad actors from trying to cash in with ill-founded or deliberately false articles and edits, Everipedia will force users to put up a token of their own in order to submit. If their work is accepted, they get their token back, plus a little bit for their contribution; if not, they lose their token. The assumption is that other users, motivated by the desire to maintain the site’s value, will actively seek to prevent such efforts….

This isn’t the first time a company has proposed a decentralized blockchain-based encyclopedia; earlier this year, a company called Lunyr announced similar plans. However, judging from Lunyr’s most recent roadmap, Everipedia will beat it to market with room to spare….(More)”.

There’s more to evidence-based policies than data: why it matters for healthcare

Curated on Posted on by Stefaan Verhulst

 at The Conversation: “The big question is: how can countries strengthen their health systems to deliver accessible, affordable and equitable care when they are often under-financed and governed in complex ways?

One answer lies in governments developing policies and programmes that are informed by evidence of what works or doesn’t. This should include what we would call “traditional data”, but should also include a broader definition of evidence. This would mean including, for example, information from citizens and stakeholders as well as programme evaluations. In this way, policies can be made more relevant for the people they affect.

Globally there is an increasing appreciation for this sort of policymaking that relies of a broader definition of evidence. Countries such as South Africa, Ghana and Thailand provide good examples.

What is evidence?

Using evidence to inform the development of health care has grown out of the use of science to choose the best decisions. It is based on data being collected in a methodical way. This approach is useful but it can’t always be neatly applied to policymaking. There are several reasons for this.

The first is that there are many different types of evidence. Evidence is more than data, even though the terms are often used to mean the same thing. For example, there is statistical and administrative data, research evidence, citizen and stakeholder information as well as programme evaluations.

The challenge is that some of these are valued more than others. More often than not, statistical data is more valued in policymaking. But both researchers and policymakers must acknowledge that for policies to be sound and comprehensive, different phases of policymaking process would require different types of evidence.

Secondly, data-as-evidence is only one input into policymaking. Policymakers face a long list of pressures they must respond to, including time, resources, political obligations and unplanned events.

Researchers may push technically excellent solutions designed in research environments. But policymakers may have other priorities in mind: are the solutions being put to them practical and affordable?Policymakers also face the limitations of having to balance various constituents while straddling the constraints of the bureaucracies they work in.

Researchers must recognise that policymakers themselves are a source of evidence of what works or doesn’t. They are able to draw on their own experiences, those of their constituents, history and their contextual knowledge of the terrain.

What this boils down to is that for policies that are based on evidence to be effective, fewer ‘push/pull’ models of evidence need to be used. Instead the models where evidence is jointly fashioned should be employed.

This means that policymakers, researchers and other key actors (like health managers or communities) must come together as soon as a problem is identified. They must first understand each other’s ideas of evidence and come to a joint conclusion of what evidence would be appropriate for the solution.

In South Africa, for example, the Department of Environmental Affairshas developed a four-phase process to policymaking. In the first phase, researchers and policymakers come together to set the agenda and agree on the needed solution. Their joint decision is then reviewed before research is undertaken and interpreted together….(More)”.

A New City O/S: The Power of Open, Collaborative, and Distributed Governance

Curated on Posted on by Stefaan Verhulst

Book by Stephen Goldsmith and Neil Kleiman: “At a time when trust is dropping precipitously and American government at the national level has fallen into a state of long-term, partisan-based gridlock, local government can still be effective—indeed more effective and even more responsive to the needs of its citizens. Based on decades of direct experience and years studying successful models around the world, the authors of this intriguing book propose a new operating system (O/S) for cities. Former mayor and Harvard professor Stephen Goldsmith and New York University professor Neil Kleiman suggest building on the giant leaps that have been made in technology, social engagement, and big data.

Calling their approach “distributed governance,” Goldsmith and Kleiman offer a model that allows public officials to mobilize new resources, surface ideas from unconventional sources, and arm employees with the information they need to become pre-emptive problem solvers. This book highlights lessons from the many innovations taking place in today’s cities to show how a new O/S can create systemic transformation.

For students of government, A New City O/S: The Power of Distributed Governance presents a groundbreaking strategy for rethinking the governance of cities, marking an important evolution of the current bureaucratic authority-based model dating from the 1920s. More important, the book is designed for practitioners, starting with public-sector executives, managers, and frontline workers. By weaving real-life examples into a coherent model, the authors have created a step-by-step guide for all those who would put the needs of citizens front and center. Nothing will do more to restore trust in government than solutions that work. A New City O/S: The Power of Distributed Governanceputs those solutions within reach of those public officials responsible for their delivery….(More)”.

Blockchain: Unpacking the disruptive potential of blockchain technology for human development.

Curated on Posted on by Stefaan Verhulst

IDRC white paper: “In the scramble to harness new technologies to propel innovation around the world, artificial intelligence, robotics, machine learning, and blockchain technologies are being explored and deployed in a wide variety of contexts globally.

Although blockchain is one of the most hyped of these new technologies, it is also perhaps the least understood. Blockchain is the distributed ledger — a database that is shared across multiple sites or institutions to furnish a secure and transparent record of events occurring during the provision of a service or contract — that supports cryptocurrencies (digital assets designed to work as mediums of exchange).

Blockchain is now underpinning applications such as land registries and identity services, but as its popularity grows, its relevance in addressing socio-economic gaps and supporting development targets like the globally-recognized UN Sustainable Development Goals is critical to unpack. Moreover, for countries in the global South that want to be more than just end users or consumers, the complex infrastructure requirements and operating costs of blockchain could prove challenging. For the purposes of real development, we need to not only understand how blockchain is workable, but also who is able to harness it to foster social inclusion and promote democratic governance.

This white paper explores the potential of blockchain technology to support human development. It provides a non-technical overview, illustrates a range of applications, and offers a series of conclusions and recommendations for additional research and potential development programming….(More)”.

Decoding Data Use: What evidence do world leaders want to achieve their goals?

Curated on Posted on by Stefaan Verhulst

Paper by Samantha Custer, Takaaki Masaki, and Carolyn Iwicki: “Information is “never the hero”, but it plays a supporting role in how leaders allocate scarce resources and accelerate development in their communities. Even in low- and middle-income countries, decision-makers have ample choices in sourcing evidence from a growing field of domestic and international data providers. However, more information is not necessarily better if it misses the mark for what leaders need to monitor their country’s progress. Claims that information is the “world’s most valuable resource” and calls for a “data revolution” will ring hollow if we can’t decode what leaders actually use — and why.

In a new report, Decoding Data Use: How leaders source data and use it to accelerate development, AidData reveals what 3500 leaders from 126 countries have to say about the types of data or analysis they use, from what sources, and for which purposes in the context of their work.  We analyze responses to AidData’s 2017 Listening to Leaders (LTL) Survey to offer insights to help funders, producers, advocates, and infomediaries of development data understand how to position themselves for greater impact….(more)”.