Global

A New Wave of Deliberative Democracy

Curated on November 27, 2019November 27, 2019 by Stefaan Verhulst

Essay by Claudia Chwalisz: “….Deliberative bodies such as citizens’ councils, assemblies, and juries are often called “deliberative mini-publics” in academic literature. They are just one aspect of deliberative democracy and involve randomly selected citizens spending a significant period of time developing informed recommendations for public authorities. Many scholars emphasize two core defining features: deliberation (careful and open discussion to weigh the evidence about an issue) and representativeness, achieved through sortition (random selection).

Of course, the principles of deliberation and sortition are not new. Rooted in ancient Athenian democracy, they were used throughout various points of history until around two to three centuries ago. Evoked by the Greek statesman Pericles in 431 BCE, the ideas—that “ordinary citizens, though occupied with the pursuits of industry, are still fair judges of public matters” and that instead of being a “stumbling block in the way of action . . . [discussion] is an indispensable preliminary to any wise action at all”—faded to the background when elections came to dominate the contemporary notion of democracy.

But the belief in the ability of ordinary citizens to deliberate and participate in public decisionmaking has come back into vogue over the past several decades. And it is modern applications of the principles of sortition and deliberation, meaning their adaption in the context of liberal representative democratic institutions, that make them “democratic innovations” today. This is not to say that there are no longer proponents who claim that governance should be the domain of “experts” who are committed to govern for the general good and have superior knowledge to do it. Originally espoused by Plato, the argument in favor of epistocracy—rule by experts—continues to be reiterated, such as in Jason Brennan’s 2016 book Against Democracy. It is a reminder that the battle of ideas for democracy’s future is nothing new and requires constant engagement.

Today’s political context—characterized by political polarization; mistrust in politicians, governments, and fellow citizens; voter apathy; increasing political protests; and a new context of misinformation and disinformation—has prompted politicians, policymakers, civil society organizations, and citizens to reflect on how collective public decisions are being made in the twenty-first century. In particular, political tensions have raised the need for new ways of achieving consensus and taking action on issues that require long-term solutions, such as climate change and technology use. Assembling ordinary citizens from all parts of society to deliberate on a complex political issue has thus become even more appealing.

Some discussions have returned to exploring democracy’s deliberative roots. An ongoing study by the Organization for Economic Co-operation and Development (OECD) is analyzing over 700 cases of deliberative mini-publics commissioned by public authorities to inform their decisionmaking. The forthcoming report assesses the mini-publics’ use, principles of good practice, and routes to institutionalization.³ This new area of work stems from the 2017 OECD Recommendation of the Council on Open Government, which recommends that adherents (OECD members and some nonmembers) grant all stakeholders, including citizens, “equal and fair opportunities to be informed and consulted and actively engage them in all phases of the policy-cycle” and “promote innovative ways to effectively engage with stakeholders to source ideas and co-create solutions.” A better understanding of how public authorities have been using deliberative mini-publics to inform their decisionmaking around the world, not just in OECD countries, should provide a richer understanding of what works and what does not. It should also reveal the design principles needed for mini-publics to effectively function, deliver strong recommendations, increase legitimacy of the decisionmaking process, and possibly even improve public trust….(More)”.

Seeing Like a Finite State Machine

Curated on November 25, 2019November 27, 2019 by Stefaan Verhulst

Henry Farrell at the Crooked Timber: “…So what might a similar analysis say about the marriage of authoritarianism and machine learning? Something like the following, I think. There are two notable problems with machine learning. One – that while it can do many extraordinary things, it is not nearly as universally effective as the mythology suggests. The other is that it can serve as a magnifier for already existing biases in the data. The patterns that it identifies may be the product of the problematic data that goes in, which is (to the extent that it is accurate) often the product of biased social processes. When this data is then used to make decisions that may plausibly reinforce those processes (by singling e.g. particular groups that are regarded as problematic out for particular police attention, leading them to be more liable to be arrested and so on), the bias may feed upon itself.

This is a substantial problem in democratic societies, but it is a problem where there are at least some counteracting tendencies. The great advantage of democracy is its openness to contrary opinions and divergent perspectives. This opens up democracy to a specific set of destabilizing attacks but it also means that there are countervailing tendencies to self-reinforcing biases. When there are groups that are victimized by such biases, they may mobilize against it (although they will find it harder to mobilize against algorithms than overt discrimination). When there are obvious inefficiencies or social, political or economic problems that result from biases, then there will be ways for people to point out these inefficiencies or problems.

These correction tendencies will be weaker in authoritarian societies; in extreme versions of authoritarianism, they may barely even exist. Groups that are discriminated against will have no obvious recourse. Major mistakes may go uncorrected: they may be nearly invisible to a state whose data is polluted both by the means employed to observe and classify it, and the policies implemented on the basis of this data. A plausible feedback loop would see bias leading to error leading to further bias, and no ready ways to correct it. This of course, will be likely to be reinforced by the ordinary politics of authoritarianism, and the typical reluctance to correct leaders, even when their policies are leading to disaster. The flawed ideology of the leader (We must all study Comrade Xi thought to discover the truth!) and of the algorithm (machine learning is magic!) may reinforce each other in highly unfortunate ways.

In short, there is a very plausible set of mechanisms under which machine learning and related techniques may turn out to be a disaster for authoritarianism, reinforcing its weaknesses rather than its strengths, by increasing its tendency to bad decision making, and reducing further the possibility of negative feedback that could help correct against errors. This disaster would unfold in two ways. The first will involve enormous human costs: self-reinforcing bias will likely increase discrimination against out-groups, of the sort that we are seeing against the Uighur today. The second will involve more ordinary self-ramifying errors, that may lead to widespread planning disasters, which will differ from those described in Scott’s account of High Modernism in that they are not as immediately visible, but that may also be more pernicious, and more damaging to the political health and viability of the regime for just that reason….(More)”

Manual of Digital Earth

Curated on November 24, 2019November 24, 2019 by Stefaan Verhulst

Book by Huadong Guo, Michael F. Goodchild and Alessandro Annoni: “This open access book offers a summary of the development of Digital Earth over the past twenty years. By reviewing the initial vision of Digital Earth, the evolution of that vision, the relevant key technologies, and the role of Digital Earth in helping people respond to global challenges, this publication reveals how and why Digital Earth is becoming vital for acquiring, processing, analysing and mining the rapidly growing volume of global data sets about the Earth.

The main aspects of Digital Earth covered here include: Digital Earth platforms, remote sensing and navigation satellites, processing and visualizing geospatial information, geospatial information infrastructures, big data and cloud computing, transformation and zooming, artificial intelligence, Internet of Things, and social media. Moreover, the book covers in detail the multi-layered/multi-faceted roles of Digital Earth in response to sustainable development goals, climate changes, and mitigating disasters, the applications of Digital Earth (such as digital city and digital heritage), the citizen science in support of Digital Earth, the economic value of Digital Earth, and so on. This book also reviews the regional and national development of Digital Earth around the world, and discusses the role and effect of education and ethics. Lastly, it concludes with a summary of the challenges and forecasts the future trends of Digital Earth.By sharing case studies and a broad range of general and scientific insights into the science and technology of Digital Earth, this book offers an essential introduction for an ever-growing international audience….(More)”.

The Right to Be Seen

Curated on November 24, 2019November 24, 2019 by Stefaan Verhulst

Anne-Marie Slaughter and Yuliya Panfil at Project Syndicate: “While much of the developed world is properly worried about myriad privacy outrages at the hands of Big Tech and demanding – and securing – for individuals a “right to be forgotten,” many around the world are posing a very different question: What about the right to be seen?

Just ask the billion people who are locked out of services we take for granted – things like a bank account, a deed to a house, or even a mobile phone account – because they lack identity documents and thus can’t prove who they are. They are effectively invisible as a result of poor data.

The ability to exercise many of our most basic rights and privileges – such as the right to vote, drive, own property, and travel internationally – is determined by large administrative agencies that rely on standardized information to determine who is eligible for what. For example, to obtain a passport it is typically necessary to present a birth certificate. But what if you do not have a birth certificate? To open a bank account requires proof of address. But what if your house doesn’t have an address?

The inability to provide such basic information is a barrier to stability, prosperity, and opportunity. Invisible people are locked out of the formal economy, unable to vote, travel, or access medical and education benefits. It’s not that they are undeserving or unqualified, it’s that they are data poor.

In this context, the rich digital record provided by our smartphones and other sensors could become a powerful tool for good, so long as the risks are acknowledged. These gadgets, which have become central to our social and economic lives, leave a data trail that for many of us is the raw material that fuels what Harvard’s Shoshana Zuboff calls “surveillance capitalism.” Our Google location history shows exactly where we live and work. Our email activity reveals our social networks. Even the way we hold our smartphone can give away early signs of Parkinson’s.

But what if citizens could harness the power of these data for themselves, to become visible to administrative gatekeepers and access the rights and privileges to which they are entitled? Their virtual trail could then be converted into proof of physical facts.

That is beginning to happen. In India, slum dwellers are using smartphone location data to put themselves on city maps for the first time and register for addresses that they can then use to receive mail and register for government IDs. In Tanzania, citizens are using their mobile payment histories to build their credit scores and access more traditional financial services. And in Europe and the United States, Uber drivers are fighting for their rideshare data to advocate for employment benefits….(More)”.

“Mind the Five”: Guidelines for Data Privacy and Security in Humanitarian Work With Undocumented Migrants and Other Vulnerable Populations

Curated on November 24, 2019November 24, 2019 by Stefaan Verhulst

Paper by Sara Vannini, Ricardo Gomez and Bryce Clayton Newell: “The forced displacement and transnational migration of millions of people around the world is a growing phenomenon that has been met with increased surveillance and datafication by a variety of actors. Small humanitarian organizations that help irregular migrants in the United States frequently do not have the resources or expertise to fully address the implications of collecting, storing, and using data about the vulnerable populations they serve. As a result, there is a risk that their work could exacerbate the vulnerabilities of the very same migrants they are trying to help. In this study, we propose a conceptual framework for protecting privacy in the context of humanitarian information activities (HIA) with irregular migrants. We draw from a review of the academic literature as well as interviews with individuals affiliated with several US‐based humanitarian organizations, higher education institutions, and nonprofit organizations that provide support to undocumented migrants. We discuss 3 primary issues: (i) HIA present both technological and human risks; (ii) the expectation of privacy self‐management by vulnerable populations is problematic; and (iii) there is a need for robust, actionable, privacy‐related guidelines for HIA. We suggest 5 recommendations to strengthen the privacy protection offered to undocumented migrants and other vulnerable populations….(More)”.

National SDG Review: data challenges and opportunities

Curated on November 23, 2019November 23, 2019 by Stefaan Verhulst

Press Release: “…the Partnership in Statistics for Development in the 21st Century (PARIS21) and Partners for Review launched a landmark new paper that identifies the factors preventing countries from fully exploiting their data ecosystem and proposes solutions to strengthening statistical capacities to achieve the 2030 Agenda for Sustainable Development.

Ninety percent of the data in the world has been created in the past two years, yet many countries with low statistical capacity struggle to produce, analyse and communicate the data necessary to advance sustainable development. At the same time, demand for more and better data and statistics is increasingly massively, with international agreements like the 2030 Agenda placing unprecedented demand on countries to report on more than 230 indicators.

Using PARIS21’s Capacity Development 4.0 (CD 4.0) approach, the paper shows that leveraging data available in the data ecosystem for official reporting requires new capacity in terms of skills and knowledge, management, politics and power. The paper also shows that these capacities need to be developed at both the organisational and systemic level, which involves the various channels and interactions that connect different organisations.

Aimed at national statistics offices, development professionals and others involved in the national data ecosystem, the paper provides a roadmap that can help national statistical systems develop and strengthen the capacities of traditional and new actors in the data ecosystem to improve both the follow-up and review process of the 2030 Agenda as well as the data architecture for sustainable development at the national level…(More)”.

Why Data Is Not the New Oil

Curated on November 23, 2019November 25, 2019 by Stefaan Verhulst

Blogpost by Alec Stapp: “Data is the new oil,” said Jaron Lanier in a recent op-ed for The New York Times. Lanier’s use of this metaphor is only the latest instance of what has become the dumbest meme in tech policy. As the digital economy becomes more prominent in our lives, it is not unreasonable to seek to understand one of its most important inputs. But this analogy to the physical economy is fundamentally flawed. Worse, introducing regulations premised upon faulty assumptions like this will likely do far more harm than good. Here are seven reasons why “data is the new oil” misses the mark:

1. Oil is rivalrous; data is non-rivalrous

If someone uses a barrel of oil, it can’t be consumed again. But, as Alan McQuinn, a senior policy analyst at the Information Technology and Innovation Foundation, noted, “when consumers ‘pay with data’ to access a website, they still have the same amount of data after the transaction as before. As a result, users have an infinite resource available to them to access free online services.” Imposing restrictions on data collection makes this infinite resource finite.

2. Oil is excludable; data is non-excludable

Oil is highly excludable because, as a physical commodity, it can be stored in ways that prevent use by non-authorized parties. However, as my colleagues pointed out in a recent comment to the FTC: “While databases may be proprietary, the underlying data usually is not.” They go on to argue that this can lead to under-investment in data collection:

[C]ompanies that have acquired a valuable piece of data will struggle both to prevent their rivals from obtaining the same data as well as to derive competitive advantage from the data. For these reasons, it also means that firms may well be more reluctant to invest in data generation than is socially optimal. In fact, to the extent this is true there is arguably more risk of companies under-investing in data generation than of firms over-investing in order to create data troves with which to monopolize a market. This contrasts with oil, where complete excludability is the norm.

3. Oil is fungible; data is non-fungible

Oil is a commodity, so, by definition, one barrel of oil of a given grade is equivalent to any other barrel of that grade. Data, on the other hand, is heterogeneous. Each person’s data is unique and may consist of a practically unlimited number of different attributes that can be collected into a profile. This means that oil will follow the law of one price, while a dataset’s value will be highly contingent on its particular properties and commercialization potential.

4. Oil has positive marginal costs; data has zero marginal costs

There is a significant expense to producing and distributing an additional barrel of oil (as low as $5.49 per barrel in Saudi Arabia; as high as $21.66 in the U.K.). Data is merely encoded information (bits of 1s and 0s), so gathering, storing, and transferring it is nearly costless (though, to be clear, setting up systems for collecting and processing can be a large fixed cost). Under perfect competition, the market clearing price is equal to the marginal cost of production (hence why data is traded for free services and oil still requires cold, hard cash)….(More)”.

Principles alone cannot guarantee ethical AI

Curated on November 23, 2019November 25, 2019 by Stefaan Verhulst

Paper by Brent Mittelstadt: “Artificial intelligence (AI) ethics is now a global topic of discussion in academic and policy circles. At least 84 public–private initiatives have produced statements describing high-level principles, values and other tenets to guide the ethical development, deployment and governance of AI. According to recent meta-analyses, AI ethics has seemingly converged on a set of principles that closely resemble the four classic principles of medical ethics. Despite the initial credibility granted to a principled approach to AI ethics by the connection to principles in medical ethics, there are reasons to be concerned about its future impact on AI development and governance. Significant differences exist between medicine and AI development that suggest a principled approach for the latter may not enjoy success comparable to the former. Compared to medicine, AI development lacks (1) common aims and fiduciary duties, (2) professional history and norms, (3) proven methods to translate principles into practice, and (4) robust legal and professional accountability mechanisms. These differences suggest we should not yet celebrate consensus around high-level principles that hide deep political and normative disagreement….(More)”.

Surveillance giants: how the business model of Google and Facebook threatens human rights

Curated on November 23, 2019November 27, 2019 by Stefaan Verhulst

Report by Amnesty International: “Google and Facebook help connect the world and provide crucial services to billions. To participate meaningfully in today’s economy and society, and to realize their human rights, people rely on access to the internet—and to the tools Google and Facebook offer. But Google and Facebook’s platforms come at a systemic cost. The companies’ surveillance-based business model is inherently incompatible with the right to privacy and poses a threat to a range of other rights including freedom of opinion and expression, freedom of thought, and the right to equality and non-discrimination….(More)”.