Stefaan Verhulst
Article by Joseph Fridman: “…At precisely the same moment in which the idea of predictive control has risen to dominance within the corporate sphere, it’s also gained a remarkable following within cognitive science. According to an increasingly influential school of neuroscientists, who orient themselves around the idea of the ‘predictive brain’, the essential activity of our most important organ is to produce a constant stream of predictions: predictions about the noises we’ll hear, the sensations we’ll feel, the objects we’ll perceive, the actions we’ll perform and the consequences that will follow. Taken together, these expectations weave the tapestry of our reality – in other words, our guesses about what we’ll see in the world become the world we see. Almost 400 years ago, with the dictum ‘I think, therefore I am,’ René Descartes claimed that cognition was the foundation of the human condition. Today, prediction has taken its place. As the cognitive scientist Anil Seth put it: ‘I predict (myself) therefore I am.’
Somehow, the logic we find animating our bodies is the same one transforming our body politic. The prediction engine – the conceptual tool used by today’s leading brain scientists to understand the deepest essence of our humanity – is also the one wielded by today’s most powerful corporations and governments. How did this happen and what does it mean?
One explanation for this odd convergence emerges from a wider historical tendency: humans have often understood the nervous system in terms of the flourishing technologies of their era, as the scientist and historian Matthew Cobb explained in The Idea of the Brain (2020). Thomas Hobbes, in his book Leviathan (1651), likened human bodies to ‘automata’, ‘[e]ngines that move themselves by springs and wheeles as doth a watch’. What is the heart, Hobbes asked, if not ‘a Spring; and the Nerves, but so many Strings …?’ Similarly, Descartes described animal spirits moving through the nerves according to the same physical properties that animated the hydraulic machines he witnessed on display in the French royal gardens.
The rise of electronic communications systems accelerated this trend. In the middle of the 19th century, the surgeon and chemist Alfred Smee said the brain was made up of batteries and photovoltaic circuits, allowing the nervous system to conduct ‘electro-telegraphic communication’ with the body. Towards the turn of the 20th century, the neuroscientist Santiago Ramón y Cajal described the positioning of different neural structures ‘somewhat as a telegraph pole supports the conducting wire’. And, during the First World War, the British Royal Institution Christmas lectures featured the anatomist and anthropologist Arthur Keith, who compared brain cells to operators in a telephonic exchange.
The technologies that have come to dominate many of our lives today are not primarily hydraulic or photovoltaic, or even telephonic or electro-telegraphic. They’re not even computational in any simplistic sense. They are predictive, and their infrastructures construct and constrain behaviour in all spheres of life. The old layers remain – electrical wiring innervates homes and workplaces, and water flows into sinks and showers through plumbing hidden from view. But these infrastructures are now governed by predictive technologies, and they don’t just guide the delivery of materials, but of information. Predictive models construct the feeds we scroll; they autocomplete our texts and emails, prompt us to leave for work on time, and pick out the playlists we listen to on the commute that they’ve plotted out for us. Consequential decisions in law enforcement, military and financial contexts are increasingly influenced by automated assessments spat out by proprietary predictive engines….(More)”.
Paper by Geoff Boeing: “Urban planning and morphology have relied on analytical cartography and visual communication tools for centuries to illustrate spatial patterns, conceptualize proposed designs, compare alternatives, and engage the public. Classic urban form visualizations – from Giambattista Nolli’s ichnographic maps of Rome to Allan Jacobs’s figure-ground diagrams of city streets – have compressed physical urban complexity into easily comprehensible information artifacts. Today we can enhance these traditional workflows through the Smart Cities paradigm of understanding cities via user-generated content and harvested data in an information management context. New spatial technology platforms and big data offer new lenses to understand, evaluate, monitor, and manage urban form and evolution. This paper builds on the theoretical framework of visual cultures in urban planning and morphology to introduce and situate computational data science processes for exploring urban fabric patterns and spatial order. It demonstrates these workflows with OSMnx and data from OpenStreetMap, a collaborative spatial information system and mapping platform, to examine street network patterns, orientations, and configurations in different study sites around the world, considering what these reveal about the urban fabric. The age of ubiquitous urban data and computational toolkits opens up a new era of worldwide urban form analysis from integrated quantitative and qualitative perspectives….(More)”.
Essay by Maria Savona: “The growth of interest in personal data has been unprecedented. Issues of privacy violation, power abuse, practices of electoral behaviour manipulation unveiled in the Cambridge Analytica scandal, and a sense of imminent impingement of our democracies are at the forefront of policy debates. Yet, these concerns seem to overlook the issue of concentration of equity value (stemming from data value, which I use interchangeably here) that underpins the current structure of big tech business models. Whilst these quasi-monopolies own the digital infrastructure, they do not own the personal data that provide the raw material for data analytics.
The European Commission has been at the forefront of global action to promote convergence of the governance of data (privacy), including, but not limited to, the General Data Protection Regulation (GDPR) (European Commission 2016), enforced in May 2018. Attempts to enforce similar regulations are emerging around the world, including the California Consumer Privacy Act, which came into effect on 1 January 2020. Notwithstanding greater awareness among citizens around the use of their data, companies find that complying with GDPR is, at best, a useless nuisance.
Data have been seen as ‘innovation investment’ since the beginning of the 1990s. The first edition of the Oslo Manual, the OECD’s international guidelines for collecting and using data on innovation in firms, dates back to 19921 and included the collection of databases on employee best practices as innovation investments. Data are also measured as an ‘intangible asset’ (Corrado et al. 2009 was one of the pioneering studies). What has changed over the last decade? The scale of data generation today is such that its management and control might have already gone well beyond the capacity of the very tech giants we are all feeding. Concerns around data governance and data privacy might be too little and too late.
In this column, I argue that economists have failed twice: first, to predict the massive concentration of data value in the hands of large platforms; and second, to account for the complexity of the political economy aspects of data accumulation. Based on a pair of recent papers (Savona 2019a, 2019b), I systematise recent research and propose a novel data rights approach to redistribute data value whilst not undermining the range of ethical, legal, and governance challenges that this poses….(More)”.
Paper by Thea Snow at Data & Policy: “Algorithmic decision tools (ADTs) are being introduced into public sector organizations to support more accurate and consistent decision-making. Whether they succeed turns, in large part, on how administrators use these tools. This is one of the first empirical studies to explore how ADTs are being used by Street Level Bureaucrats (SLBs). The author develops an original conceptual framework and uses in-depth interviews to explore whether SLBs are ignoring ADTs (algorithm aversion); deferring to ADTs (automation bias); or using ADTs together with their own judgment (an approach the author calls “artificing”). Interviews reveal that artificing is the most common use-type, followed by aversion, while deference is rare. Five conditions appear to influence how practitioners use ADTs: (a) understanding of the tool (b) perception of human judgment (c) seeing value in the tool (d) being offered opportunities to modify the tool (e) alignment of tool with expectations….(More)”.
Shoshana Zuboff in the New York Times: “Two decades ago, the American government left democracy’s front door open to California’s fledgling internet companies, a cozy fire lit in welcome. In the years that followed, a surveillance society flourished in those rooms, a social vision born in the distinct but reciprocal needs of public intelligence agencies and private internet companies, both spellbound by a dream of total information awareness. Twenty years later, the fire has jumped the screen, and on Jan. 6, it threatened to burn down democracy’s house.
I have spent exactly 42 years studying the rise of the digital as an economic force driving our transformation into an information civilization. Over the last two decades, I’ve observed the consequences of this surprising political-economic fraternity as those young companies morphed into surveillance empires powered by global architectures of behavioral monitoring, analysis, targeting and prediction that I have called surveillance capitalism. On the strength of their surveillance capabilities and for the sake of their surveillance profits, the new empires engineered a fundamentally anti-democratic epistemic coupmarked by unprecedented concentrations of knowledge about us and the unaccountable power that accrues to such knowledge.
In an information civilization, societies are defined by questions of knowledge — how it is distributed, the authority that governs its distribution and the power that protects that authority. Who knows? Who decides who knows? Who decides who decides who knows? Surveillance capitalists now hold the answers to each question, though we never elected them to govern. This is the essence of the epistemic coup. They claim the authority to decide who knows by asserting ownership rights over our personal information and defend that authority with the power to control critical information systems and infrastructures….(More)”.
Paper by Zachary D. Kaufman: “Modern technology enables people to view, document, and share evidence of crimes contemporaneously or soon after commission. Electronic transmission of this material — including through social media and mobile devices — raises legal, moral, and practical questions about spectators’ responsibilities. In the digital age, will these actors be bystanders or upstanders? What role can and should the law play in shaping their behavior?
This Article argues that certain witnesses who are not physically present at the scene of a crime should be held criminally accountable for failing to report specified violent offenses. Focusing on rape, police brutality, and other misconduct, this Article demonstrates that recent technological innovations create new opportunities and challenges to pursue justice and accountability. Such culpability centers on “Bad Samaritan laws”: statutes that impose a legal duty to assist others in peril through intervening directly (also known as “the duty to rescue”) or notifying authorities (also known as “the duty to report”). However, many of these antiquated laws arguably apply only to witnesses who are physically present, which limits their potential effectiveness today.
Not all virtual witnesses should be subject to liability. To consider which categories of actors may warrant criminal punishment, this Article introduces a novel typology of bystanders and upstanders in the digital age. This typology draws on an original case study of the first known sexual crime livestreamed in the United States by a third party, which more than 700 people viewed. Harnessing insights from that case study and other episodes, the Article recommends that legislators should modernize, refine, and proliferate Bad Samaritan laws and that law enforcement should enforce these statutes or leverage them to obtain witness testimony. To that end, the Article proposes a model duty-to-report statute that includes features such as applicability to virtual presence and reasoned exemptions for noncompliance….(More)”.
Paper by Emily Kubin, Curtis Puryear, Chelsea Schein, and Kurt Gray: “All Americans are affected by rising political polarization, whether because of a gridlocked Congress or antagonistic holiday dinners. People believe that facts are essential for earning the respect of political adversaries, but our research shows that this belief is wrong. We find that sharing personal experiences about a political issue—especially experiences involving harm—help to foster respect via increased perceptions of rationality. This research provides a straightforward pathway for increasing moral understanding and decreasing political intolerance. These findings also raise questions about how science and society should understand the nature of truth in the era of “fake news.” In moral and political disagreements, everyday people treat subjective experiences as truer than objective facts….(More)”
Paper by Carla Bonina, Kari Koskinen, Ben Eaton, and Annabelle Gawer: “Digital platforms hold a central position in today’s world economy and are said to offer a great potential for the economies and societies in the global South. Yet, to date, the scholarly literature on digital platforms has largely concentrated on business while their developmental implications remain understudied. In part, this is because digital platforms are a challenging research object due to their lack of conceptual definition, their spread across different regions and industries, and their intertwined nature with institutions, actors and digital technologies. The purpose of this article is to contribute to the ongoing debate in information systems and ICT4D research to understand what digital platforms mean for development. To do so, we first define what digital platforms are and differentiate between transaction and innovation platforms, and explain their key characteristics in terms of purpose, research foundations, material properties and business models. We add the socio‐technical context digital platforms operate and the linkages to developmental outcomes. We then conduct an extensive review to explore what current areas, developmental goals, tensions and issues emerge in the literature on platforms and development and identify relevant gaps in our knowledge. We later elaborate on six research questions to advance the studies on digital platforms for development: on indigenous innovation, digital platforms and institutions, on exacerbation of inequalities, on alternative forms of value, on the dark side of platforms and on the applicability of the platform typology for development….(More)”.
Blog Post by Jasper Tjaden, Andres Arau, Muertizha Nuermaimaiti, Imge Cetin, Eduardo Acostamadiedo, Marzia Rango: Act 1 — High Expectations
“Data is the new oil,” they say. ‘Big Data’ is even bigger than that. The “data revolution” will contribute to solving societies’ problems and help governments adopt better policies and run more effective programs. In the migration field, digital trace data are seen as a potentially powerful tool to improve migration management processes (visa applications; asylum decision and geographic allocation of asylum seeker, facilitating integration, “smart borders” etc.).1
Forecasting migration is one particular area where big data seems to excite data nerds (like us) and policymakers alike. If there is one way big data has already made a difference, it is its ability to bring different actors together — data scientists, business people and policy makers — to sit through countless slides with numbers, tables and graphs. Traditional migration data sources, like censuses, administrative data and surveys, have never quite managed to generate the same level of excitement.
Many EU countries are currently heavily investing in new ways to forecast migration. Relatively large numbers of asylum seekers in 2014, 2015 and 2016 strained the capacity of many EU governments. Better forecasting tools are meant to help governments prepare in advance.
In a recent European Migration Network study, 10 out of the 22 EU governments surveyed said they make use of forecasting methods, many using open source data for “early warning and risk analysis” purposes. The 2020 European Migration Network conference was dedicated entirely to the theme of forecasting migration, hosting more than 15 expert presentations on the topic. The recently proposed EU Pact on Migration and Asylum outlines a “Migration Preparedness and Crisis Blueprint” which “should provide timely and adequate information in order to establish the updated migration situational awareness and provide for early warning/forecasting, as well as increase resilience to efficiently deal with any type of migration crisis.” (p. 4) The European Commission is currently finalizing a feasibility study on the use of artificial intelligence for predicting migration to the EU; Frontex — the EU Border Agency — is scaling up efforts to forecast irregular border crossings; EASO — the European Asylum Support Office — is devising a composite “push-factor index” and experimenting with forecasting asylum-related migration flows using machine learning and data at scale. In Fall 2020, during Germany’s EU Council Presidency, the German Interior Ministry organized a workshop series around Migration 4.0 highlighting the benefits of various ways to “digitalize” migration management. At the same time, the EU is investing substantial resources in migration forecasting research under its Horizon2020 programme, including QuantMig, ITFLOWS, and HumMingBird.
Is all this excitement warranted?
Yes, it is….(More)” See also: Big Data for Migration Alliance
Jeffrey M. Perkel at Nature: “From Fortran to arXiv.org, these advances in programming and platforms sent biology, climate science and physics into warp speed….In 2019, the Event Horizon Telescope team gave the world the first glimpse of what a black hole actually looks like. But the image of a glowing, ring-shaped object that the group unveiled wasn’t a conventional photograph. It was computed — a mathematical transformation of data captured by radio telescopes in the United States, Mexico, Chile, Spain and the South Pole1. The team released the programming code it used to accomplish that feat alongside the articles that documented its findings, so the scientific community could see — and build on — what it had done.
It’s an increasingly common pattern. From astronomy to zoology, behind every great scientific finding of the modern age, there is a computer. Michael Levitt, a computational biologist at Stanford University in California who won a share of the 2013 Nobel Prize in Chemistry for his work on computational strategies for modelling chemical structure, notes that today’s laptops have about 10,000 times the memory and clock speed that his lab-built computer had in 1967, when he began his prizewinning work. “We really do have quite phenomenal amounts of computing at our hands today,” he says. “Trouble is, it still requires thinking.”
Enter the scientist-coder. A powerful computer is useless without software capable of tackling research questions — and researchers who know how to write it and use it. “Research is now fundamentally connected to software,” says Neil Chue Hong, director of the Software Sustainability Institute, headquartered in Edinburgh, UK, an organization dedicated to improving the development and use of software in science. “It permeates every aspect of the conduct of research.”
Scientific discoveries rightly get top billing in the media. But Nature this week looks behind the scenes, at the key pieces of code that have transformed research over the past few decades.
Although no list like this can be definitive, we polled dozens of researchers over the past year to develop a diverse line-up of ten software tools that have had a big impact on the world of science. You can weigh in on our choices at the end of the story….(More)”.