Stefaan Verhulst

Essay by Frank Pasquale: “…Who needs city housing regulators when AirBnB can use data-driven methods to effectively regulate room-letting, then house-letting, and eventually urban planning generally? Why not let Amazon have its own jurisdiction or charter city, or establish special judicial procedures for Foxconn? Some vanguardists of functional sovereignty believe online rating systems could replace state occupational licensure—so rather than having government boards credential workers, a platform like LinkedIn could collect star ratings on them.

In this and later posts, I want to explain how this shift from territorial to functional sovereignty is creating a new digital political economy. Amazon’s rise is instructive. As Lina Khan explains, “the company has positioned itself at the center of e-commerce and now serves as essential infrastructure for a host of other businesses that depend upon it.” The “everything store” may seem like just another service in the economy—a virtual mall. But when a firm combines tens of millions of customers with a “marketing platform, a delivery and logistics network, a payment service, a credit lender, an auction house…a hardware manufacturer, and a leading host of cloud server space,” as Khan observes, it’s not just another shopping option.

Digital political economy helps us understand how platforms accumulate power. With online platforms, it’s not a simple narrative of “best service wins.” Network effects have been on the cyberlaw (and digital economics) agenda for over twenty years. Amazon’s dominance has exhibited how network effects can be self-reinforcing. The more merchants there are selling on (or to) Amazon, the better shoppers can be assured that they are searching all possible vendors. The more shoppers there are, the more vendors consider Amazon a “must-have” venue. As crowds build on either side of the platform, the middleman becomes ever more indispensable. Oh, sure, a new platform can enter the market—but until it gets access to the 480 million items Amazon sells (often at deep discounts), why should the median consumer defect to it? If I want garbage bags, do I really want to go over to Target.com to re-enter all my credit card details, create a new log-in, read the small print about shipping, and hope that this retailer can negotiate a better deal with Glad? Or do I, ala Sunstein, want a predictive shopping purveyor that intimately knows my past purchase habits, with satisfaction just a click away?
As artificial intelligence improves, the tracking of shopping into the Amazon groove will tend to become ever more rational for both buyers and sellers. Like a path through a forest trod ever clearer of debris, it becomes the natural default. To examine just one of many centripetal forces sucking money, data, and commerce into online behemoths, play out game theoretically how the possibility of online conflict redounds in Amazon’s favor. If you have a problem with a merchant online, do you want to pursue it as a one-off buyer? Or as someone whose reputation has been established over dozens or hundreds of transactions—and someone who can credibly threaten to deny Amazon hundreds or thousands of dollars of revenue each year? The same goes for merchants: The more tribute they can pay to Amazon, the more likely they are to achieve visibility in search results and attention (and perhaps even favor) when disputes come up. What Bruce Schneier said about security is increasingly true of commerce online: You want to be in the good graces of one of the neo-feudal giants who bring order to a lawless realm. Yet few hesitate to think about exactly how the digital lords might use their data advantages against those they ostensibly protect.

Forward-thinking legal thinkers are helping us grasp these dynamics. For example, Rory van Loo has described the status of the “corporation as courthouse”—that is, when platforms like Amazon run dispute resolution schemes to settle conflicts between buyers and sellers. Van Loo describes both the efficiency gains that an Amazon settlement process might have over small claims court, and the potential pitfalls for consumers (such as opaque standards for deciding cases). I believe that, on top of such economic considerations, we may want to consider the political economic origins of e-commerce feudalism. For example, as consumer rights shrivel, it’s rational for buyers to turn to Amazon (rather than overwhelmed small claims courts) to press their case. The evisceration of class actions, the rise of arbitration, boilerplate contracts—all these make the judicial system an increasingly vestigial organ in consumer disputes. Individuals rationally turn to online giants for powers to impose order that libertarian legal doctrine stripped from the state. And in so doing, they reinforce the very dynamics that led to the state’s etiolation in the first place….(More)”.

Book edited by Laurence Bherer, Pascale Dufour, and Francoise Montambeault:”Since the 1960s, participatory discourses and techniques have been at the core of decision making processes in a variety of sectors around the world – a phenomenon often referred to as the participatory turn. Over the years, this participatory turn has given birth to a large array of heterogeneous participatory practices developed by a wide variety of organizations and groups, as well as by governments. Among the best-known practices of citizen participation are participatory budgeting, citizen councils, public consultations, etc. However, these experiences are sometimes far from the original 1960s’ radical conception of participatory democracy, which had a transformative dimension and aimed to overcome unequal relationships between the state and society and emancipate and empower citizens in their daily lives.

This book addresses four sets of questions: what do participatory practices mean today?; what does it mean to participate for participants, from the perspective of citizenship building?; how the processes created by the participatory turn have affected the way political representation functions?; and does the participatory turn also mean changing relationships and dynamics among civil servants, political representatives, and citizens?

Overall, the contributions in this book illustrate and grasp the complexity of the so-called participatory turn. It shows that the participatory turn now includes several participatory democracy projects, which have different effects on the overall system depending on the principles that they advocate. This book was originally published as a special issue of the Journal of Civil Society….(More)”

Paper by Karla Hoff and James Sonam Walsh: “All over the world, people are prevented from participating fully in society through mechanisms that go beyond the structural and institutional barriers identified by rational choice theory (poverty, exclusion by law or force, taste-based and statistical discrimination, and externalities from social networks).

This essay discusses four additional mechanisms that bounded rationality can explain: (i) implicit discrimination, (ii) self-stereotyping and self-censorship, (iii) “fast thinking” adapted to underclass neighborhoods, and (iv)”adaptive preferences” in which an oppressed group views its oppression as natural or even preferred.

Stable institutions have cognitive foundations — concepts, categories, social identities, and worldviews — that function like lenses through which individuals see themselves and the world. Abolishing or reforming a discriminatory institution may have little effect on these lenses. Groups previously discriminated against by law or policy may remain excluded through habits of the mind. Behavioral economics recognizes forces of social exclusion left out of rational choice theory, and identifies ways to overcome them. Some interventions have had very consequential impact….(More)”.

Ethical guidelines from The IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems: “As the use and impact of autonomous and intelligent systems (A/IS) become pervasive, we need to establish societal and policy guidelines in order for such systems to remain human-centric, serving humanity’s values and ethical principles. These systems have to behave in a way that is beneficial to people beyond reaching functional goals and addressing technical problems. This will allow for an elevated level of trust between people and technology that is needed for its fruitful, pervasive use in our daily lives.

To be able to contribute in a positive, non-dogmatic way, we, the techno-scientific communities, need to enhance our self-reflection, we need to have an open and honest debate around our imaginary, our sets of explicit or implicit values, our institutions, symbols and representations.

Eudaimonia, as elucidated by Aristotle, is a practice that defines human well-being as the highest virtue for a society. Translated roughly as “flourishing,” the benefits of eudaimonia begin by conscious contemplation, where ethical considerations help us define how we wish to live.

Whether our ethical practices are Western (Aristotelian, Kantian), Eastern (Shinto, Confucian), African (Ubuntu), or from a different tradition, by creating autonomous and intelligent systems that explicitly honor inalienable human rights and the beneficial values of their users, we can prioritize the increase of human well-being as our metric for progress in the algorithmic age. Measuring and honoring the potential of holistic economic prosperity should become more important than pursuing one-dimensional goals like productivity increase or GDP growth….(More)”.

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)”

Stefaan G. Verhulst in Open Democracy: “…There is no doubt that #Resistance (and its associated movements) holds genuine transformative potential. But for the change it brings to be meaningful (and positive), we need to ask the question: What kind of government do we really want?

Working to maintain the status quo or simply returning to, for instance, a pre-Trump reality cannot provide for the change we need to counter the decline in trust, the rise of populism and the complex social, economic and cultural problems we face. We need a clear articulation of alternatives.  Without such an articulation, there is a danger of a certain hollowness and dispersion of energies. The call for #Resistance requires a more concrete –and ultimately more productive – program that is concerned not just with rejecting or tearing down, but with building up new institutions and governance processes. What’s needed, in short, is not simply #Resistance.

Below, I suggest six shifts that can help us reimagine governance for the twenty-first century. Several of these shifts are enabled by recent technological changes (e.g., the advent of big data, blockchain and collective intelligence) as well as other emerging methods such as design thinking, behavioral economics, and agile development.

Some of the shifts I suggest have been experimented with, but they have often been developed in an ad hoc manner without a full understanding of how they could make a more systemic impact. Part of the purpose of this paper is to begin the process of a more systematic enquiry; the following amounts to a preliminary outline or blueprint for reimagined governance for the twenty-first century.

• Shift 1: from gatekeeper to platform…
• Shift 2: from inward to user-and-problem orientation…
• Shift 3: from closed to open…
• Shift 4: from deliberation to collaboration and co-creation…
• Shift 5: from ideology to evidence-based…
• Shift 6: from centralized to distributed… (More)

New set of case studies by The GovLab: “Government at all levels — federal, state and local — collects and processes troves of data in order to administer public programs, fulfill regulatory mandates or conduct research¹. This government-held data, which often contains personally identifiable information about the individuals government serves is known as “administrative data” and it can be analyzed to evaluate and improve how government and the social sector deliver services.

For example, the Social Security Administration (SSA) collects and manages data on social, welfare and disability benefit payments of nearly the entire US population as well as data such as individual lifetime records of wages and self employment earnings. The SSA uses this administrative data for, among other things, analysis of policy interventions and to develop models to project demographic and economic characteristics of the population. State governments collect computerized hospital discharge data for both Government (Medicare and Medicaid) and commercial payers while the Department of Justice (through the Bureau of Justice Standards) collects prison admission and release data to monitor correctional populations and to address several policy questions, including those on recidivism and prisoner reentry.

Though they have long collected data, increasingly in digital form, government agencies have struggled to create the infrastructure and acquire the skills needed to make use of this administrative data to realize the promise of evidence-based policymaking.

The goal of this collection of eight case studies is to look at how governments are beginning to “get smarter” about using their own data. By comparing the ways in which they have chosen to collaborate with researchers and make often sensitive data usable to government employees and researchers in ethical and responsible ways, we hope to increase our understanding of what is required to be able to make better use of administrative data including the governance structures, technology infrastructure and key personnel. The hope is to enable other public institutions to know what is required to be able to make better use of administrative data. What follows is a summary of the learnings from those case studies. We start with an articulation of the value proposition for greater use of administrative data followed by the key learnings and the case studies themselves….(More)”

Read the case studies here.

Book by Shannon Mattern: “For years, pundits have trumpeted the earthshattering changes that big data and smart networks will soon bring to our cities. But what if cities have long been built for intelligence, maybe for millennia? In Code and Clay, Data and Dirt Shannon Mattern advances the provocative argument that our urban spaces have been “smart” and mediated for thousands of years.

Offering powerful new ways of thinking about our cities, Code and Clay, Data and Dirt goes far beyond the standard historical concepts of origins, development, revolutions, and the accomplishments of an elite few. Mattern shows that in their architecture, laws, street layouts, and civic knowledge—and through technologies including the telephone, telegraph, radio, printing, writing, and even the human voice—cities have long negotiated a rich exchange between analog and digital, code and clay, data and dirt, ether and ore.

Mattern’s vivid prose takes readers through a historically and geographically broad range of stories, scenes, and locations, synthesizing a new narrative for our urban spaces. Taking media archaeology to the city’s streets, Code and Clay, Data and Dirt reveals new ways to write our urban, media, and cultural histories….(More)”.

Book by Rachel Botsman: “If you can’t trust those in charge, who can you trust? From government to business, banks to media, trust in institutions is at an all-time low. But this isn’t the age of distrust–far from it.

OECD Report: “In 2007, the OECD Principles and Guidelines for Access to Research Data from Public Funding were published and in the intervening period there has been an increasing emphasis on open science. At the same time, the quantity and breadth of research data has massively expanded. So called “Big Data” is no longer limited to areas such as particle physics and astronomy, but is ubiquitous across almost all fields of research. This is generating exciting new opportunities, but also challenges.

The promise of open research data is that they will not only accelerate scientific discovery and improve reproducibility, but they will also speed up innovation and improve citizen engagement with research. In short, they will benefit society as a whole. However, for the benefits of open science and open research data to be realised, these data need to be carefully and sustainably managed so that they can be understood and used by both present and future generations of researchers.

Data repositories – based in local and national research institutions and international bodies – are where the long-term stewardship of research data takes place and hence they are the foundation of open science. Yet good data stewardship is costly and research budgets are limited. So, the development of sustainable business models for research data repositories needs to be a high priority in all countries. Surprisingly, perhaps, little systematic analysis has been done on income streams, costs, value propositions, and business models for data repositories, and that is the gap this report attempts to address, from a science policy perspective…..

This project was designed to take up the challenge and to contribute to a better understanding of how research data repositories are funded, and what developments are occurring in their funding. Central questions included:

• How are data repositories currently funded, and what are the key revenue sources?
• What innovative revenue sources are available to data repositories?
• How do revenue sources fit together into sustainable business models?
• What incentives for, and means of, optimising costs are available?
• What revenue sources and business models are most acceptable to key stakeholders?…(More)”