Stefaan Verhulst
Emily A. Vogels, Lee Rainie and Janna Anderson at Pew Research: “A large share of experts and analysts worry that people’s technology use will mostly weaken core aspects of democracy and democratic representation in the coming decade. Yet they also foresee significant social and civic innovation between now and 2030 to try to address emerging issues.
In this new report, technology experts who shared serious concerns for democracy in a recent Pew Research Center canvassing weigh in with their views about the likely changes and reforms that might occur in the coming years.
Overall, 697 technology innovators, developers, business and policy leaders, researchers and activists responded to the following query:
Social and civic innovation and its impact on the new difficulties of the digital age: As the Industrial Revolution swept through societies, people eventually took steps to mitigate abuses and harms that emerged. For instance, new laws were enacted to make workplaces safer and protect children; standards were created for product safety and effectiveness; new kinds of organizations came into being to help workers (e.g., labor unions) and make urban life more meaningful (e.g., settlement houses, Boys/Girls Clubs); new educational institutions were created (e.g., trade schools); household roles in families were reconfigured.
Today’s “techlash” illuminates the issues that have surfaced in the digital era. We seek your insights as to whether and how reforms to ease these problems and others might unfold.
The question: Will significant social and civic innovation occur between now and 2030? By “social and civic innovation,” we mean the creation of things like new technology tools, legal protections, social norms, new or reconfigured groups and communities, educational efforts and other strategies to address digital-age challenges.
Some 84% of these respondents say there will be significant social and civic innovation between now and 2030, while 16% say there will not be significant social and civic innovation in the timeframe.
Asked a follow-up question about whether humans’ use of technology will lead to or prevent significant social and civic innovation, 69% of these expert respondents said they expect that technology use will help significantly mitigate problems, 20% predicted that technology use will effectively prevent significant mitigation of problems and 11% responded that it is likely that technology use will have no effect on social and civic innovation.
This is a nonscientific canvassing of experts, based on a non-random sample. The results represent only the opinions of individuals who responded to the query and are not projectable to any other population. The methodology underlying this canvassing is elaborated here. The bulk of this report covers these experts’ written answers explaining their responses.
Respondents in this canvassing sound three broad themes about the changing technology landscape and how it will impact citizens’ political and social activities.
First, they predict that overall connectivity between people and their devices will increase as more digital applications emerge that allow people to create, share and observe information. This trend could accelerate as people employ smart agents and bots to interact with other people or other people’s avatars. These experts say persistent and expanded human connectivity will affect the way people engage with each other as citizens and influence how they work to build groups aimed at impacting policy and politics. Some argue this will change the way people interact with democratic institutions….(More)”.
Essay by David S. Jones and Stefan Helmreich: “…Is the most recent rise in new cases—the sharp increase in case counts and hospitalizations reported this week in several states—a second wave, or rather a second peak of a first wave? Will the world see a devastating second wave in the fall?
Such imagery of waves has pervaded talk about the propagation of the infection from the beginning. On January 29, just under a month after the first instances of COVID-19 were reported in Wuhan, Chinese health officials published a clinical report about their first 425 cases, describing them as “the first wave of the epidemic.” On March 4 the French epidemiologist Antoine Flahault asked, “Has China just experienced a herald wave, to use terminology borrowed from those who study tsunamis, and is the big wave still to come?” The Asia Times warned shortly thereafter that “a second deadly wave of COVID-19 could crash over China like a tsunami.” A tsunami, however, struck elsewhere, with the epidemic surging in Iran, Italy, France, and then the United States. By the end of April, with the United States having passed one million cases, the wave forecasts had become bleaker. Prominent epidemiologists predicted three possible future “wave scenarios”—described by one Boston reporter as “seascapes,” characterized either by oscillating outbreaks, the arrival of a “monster wave,” or a persistent and rolling crisis.
From Kristine Moore et al., “The Future of the COVID-19 Pandemic” (April 30, 2020). Used with permission from the Center for Infectious Disease Research and Policy, University of Minnesota.
While this language may be new to much of the public, the figure of the wave has long been employed to describe, analyze, and predict the behavior of epidemics. Understanding this history can help us better appreciate the conceptual inheritances of a scientific discipline suddenly at the center of public discussion. It can also help us judge the utility as well as limitations of those representations of epidemiological waves now in play in thinking about the science and policy of COVID-19. As the statistician Edward Tufte writes in his classic work The Visual Display of Quantitative Information (1983), “At their best, graphics are instruments for reasoning about quantitative information.” The wave, operating as a hybrid of the diagrammatic, mathematical, and pictorial, certainly does help to visualize and think about COVID-19 data, but it also does much more. The wave image has become an instrument for public health management and prediction—even prophecy—offering a synoptic, schematic view of the dynamics it describes.
This essay sketches this backstory of epidemic waves, which falls roughly into three eras: waves emerge first as a device of data visualization, then evolve into an object of mathematical modeling and causal investigation and finally morph into a tool of persuasion, intervention, and governance. Accounts of the wave-like rise and fall of rates of illness and death in populations first appeared in the mid-nineteenth century, with England a key player in developments that saw government officials collect data permitting the graphical tabulation of disease trends over time. During this period the wave image was primarily metaphorical, a heuristic way of talking about patterns in data. Using curving numerical plots, epidemiologists offered analogies between the spread of infection and the travel of waves, sometimes transposing the temporal tracing of epidemic data onto maps of geographical space. Exactly what mix of forces—natural or social—generated these “epidemic waves” remained a source of speculation….(More)”.
Book by Evan Michelson: “An increasingly important and often overlooked issue in science and technology policy is recognizing the role that philanthropies play in setting the direction of research. In an era where public and private resources for science are strained, the practices that foundations adopt to advance basic and applied research needs to be better understood. This first-of-its-kind study provides a detailed assessment of the current state of science philanthropy. This examination is particularly timely, given that science philanthropies will have an increasingly important and outsized role to play in advancing responsible innovation and in shaping how research is conducted.
Philanthropy and the Future of Science and Technology surveys the landscape of contemporary philanthropic involvement in science and technology by combining theoretical insights drawn from the responsible research and innovation (RRI) framework with empirical analysis investigating an array of detailed examples and case studies. Insights from interviews conducted with foundation representatives, scholars, and practitioners from a variety of sectors add real-world perspective. A wide range of philanthropic interventions are explored, focusing on support for individuals, institutions, and networks, with attention paid to the role that science philanthropies play in helping to establish and coordinate multi-sectoral funding partnerships. Novel approaches to science philanthropy are also considered, including the emergence of crowdfunding and the development of new institutional mechanisms to advance scientific research. The discussion concludes with an imaginative look into the future, outlining a series of lessons learned that can guide how new and established science philanthropies operate and envisioning alternative scenarios for the future that can inform how science philanthropy progresses over the coming decades.
This book offers a major contribution to the advancement of philanthropic investment in science and technology. Thus, it will be of considerable interest to researchers and students in public policy, public administration, political science, science and technology studies, sociology of science, and related disciplines….(More)”.
Paper by Mária Žuffová: “In election times, political parties promise in their manifestos to pass reforms increasing access to government information to root out corruption and improve public service delivery. Scholars have already offered several fascinating explanations of why governments adopt transparency policies that constrain their choices. However, knowledge of their impacts is limited. Does greater access to information deliver on its promises as an anti-corruption policy? While some research has already addressed this question in relation to freedom of information laws, the emergence of new digital technologies enabled new policies, such as open government data. Its effects on corruption remain empirically underexplored due to its novelty and a lack of measurements. In this article, I provide the first empirical study of the relationship between open government data, relative to FOI laws, and corruption. I propose a theoretical framework, which specifies conditions necessary for FOI laws and open government data to affect corruption levels, and I test it on a novel cross-country dataset.
The results suggest that the effects of open government data on corruption are conditional upon the quality of media and internet freedom. Moreover, other factors, such as free and fair elections, independent and accountable judiciary, or economic development, are far more critical for tackling corruption than increasing access to information. These findings are important for policies. In particular, digital transparency reforms will not yield results in the anti-corruption fight unless robust provisions safeguarding media and internet freedom complement them….(More)”.
Richard Thaler at a Special Edition of Organizational Behavior and Human Decision Processes: “I have long considered all my co-editors of this special issue to be good friends. That is, until they asked me to write an editorial on the topic of “what is next?” When a bunch of experts in judgment and decision-making ask you to do something they know to be impossible, you should be suspicious, right? Do they think I don’t know that predicting the future of science is impossible?
They slyly assigned Katy Milkman the job of luring me into agreeing. The first request came via email with what had to be a deliberately impenetrable subject heading: “Ask for OBHDP Special Issue You’re Co-Editing: 1–3 Paragraphs on the Future of Nudge.” The other three co-editors were copied, the message was long and complicated, and, to top it off, the first word of the subject was “Ask.” Katy surely knew there was no chance I would read that email, which of course was part of her cunning strategy. She figured that when she sent the inevitable follow-up email I would feel guilty about not responding to the first one. Guilt is a powerful nudge.
The expected second email came three days later, this time with a catchier one-word subject line: “Noodge.” (Have I mentioned that these emails arrived in the early days of the COVID-19 lockdown?) This new email began by acknowledging that the first one had been too long and poorly timed, lulling me into a false sense of security that I was being excused and off the hook. But then, Katy launched the heavy artillery. She framed her request in a way that made my acceptance the default option: “Hope you’re up for writing 1–3 paragraphs, but let me know if not and we’ll manage. :)” We all know that defaults are powerful, but did she really think this was going to work on me? Although I was mildly miffed at the brazen noodging, I find it hard to say “no” to Katy, so I stuck to my usual strategy of lying low and ignored this email as well, foolishly hoping she would give up.
That hope was dashed a week later when the third email arrived with the subject line: “pretty please with sugar on top. :)” Plus, she pulled out another trick she had up her sleeve: a deadline! “The introduction is due in just a few days!” She was telling me that this assignment, which I had never agreed to do, was almost overdue. Of course, she also knew I was trapped in my home with very few excuses. Seeing no plausible escape route at this point, I capitulated and agreed to her request.
Conclusion: nudging works! Even on me.
Recall her request was that I write one to three paragraphs. This is already the sixth paragraph so by all rights I should already be done. Certainly, I will not be lured into making any forecasts. Phil Tetlock is her colleague! But since the word processor is already open, I will instead offer a few thoughts about my hopes and dreams for this enterprise.
My first hope is that the range of “nudges” expands. We know a lot about the effect of the kinds of strategies Katy used in her emails to me such as defaults, reminders, deadlines, guilt, salience, and norms. Come to think of it, I am surprised Katy didn’t try “90 percent of all recipients of my emails agree to do what I ask.” While I concede that these ploys often (though not always) work, it can’t be that they span the entire behavioral science repertoire. So I am hoping to see studies using a different set of behavioral insights. I am sure there are good ones out there….(More)”.
Andrea Saltelli et al at Nature: “The COVID-19 pandemic illustrates perfectly how the operation of science changes when questions of urgency, stakes, values and uncertainty collide — in the ‘post-normal’ regime.
Well before the coronavirus pandemic, statisticians were debating how to prevent malpractice such as p-hacking, particularly when it could influence policy1. Now, computer modelling is in the limelight, with politicians presenting their policies as dictated by ‘science’2. Yet there is no substantial aspect of this pandemic for which any researcher can currently provide precise, reliable numbers. Known unknowns include the prevalence and fatality and reproduction rates of the virus in populations. There are few estimates of the number of asymptomatic infections, and they are highly variable. We know even less about the seasonality of infections and how immunity works, not to mention the impact of social-distancing interventions in diverse, complex societies.
Mathematical models produce highly uncertain numbers that predict future infections, hospitalizations and deaths under various scenarios. Rather than using models to inform their understanding, political rivals often brandish them to support predetermined agendas. To make sure predictions do not become adjuncts to a political cause, modellers, decision makers and citizens need to establish new social norms. Modellers must not be permitted to project more certainty than their models deserve; and politicians must not be allowed to offload accountability to models of their choosing2,3.
This is important because, when used appropriately, models serve society extremely well: perhaps the best known are those used in weather forecasting. These models have been honed by testing millions of forecasts against reality. So, too, have ways to communicate results to diverse users, from the Digital Marine Weather Dissemination System for ocean-going vessels to the hourly forecasts accumulated by weather.com. Picnickers, airline executives and fishers alike understand both that the modelling outputs are fundamentally uncertain, and how to factor the predictions into decisions.
Here we present a manifesto for best practices for responsible mathematical modelling. Many groups before us have described the best ways to apply modelling insights to policies, including for diseases4 (see also Supplementary information). We distil five simple principles to help society demand the quality it needs from modelling….(More)”.
United Nations: “As structural UN reforms consolidate, we are focused on building the data, digital, technology and innovation capabilities that the UN needs to succeed in the 21st century. The Secretary General’s “Data Strategy for Action by Everyone, Everywhere” is our agenda for the data-driven transformation.
Data permeates all aspects of our work, and its power—harnessed responsibly—is critical to the global agendas we serve. The UN family’s footprint, expertise and connectedness create unique opportunities to advance global “data action” with insight, impact and integrity. To help unlock more potential, 50 UN entities jointly designed this Strategy as a comprehensive playbook for data-driven change based on global best practice…
Our strategy pursues a simple idea: we focus not on process, but on learning, iteratively, to deliver data use cases that add value for stakeholders based on our vision, outcomes and principles. Use cases – purposes for which data is used – already permeate our organization. We will systematically identify and deliver them through dedicated data action portfolios. While new capabilities will in part emerge through “learning by doing”, we will also strengthen organizational enablers to deliver on our vision, including shifts in people and culture, partnerships, data governance and technology….(More)”.
The Economist: “In 1993 this newspaper told the world to watch the skies. At the time, humanity’s knowledge of asteroids that might hit the Earth was woefully inadequate. Like nuclear wars and large volcanic eruptions, the impacts of large asteroids can knock seven bells out of the climate; if one thereby devastated a few years’ worth of harvests around the globe it would kill an appreciable fraction of the population. Such an eventuality was admittedly highly unlikely. But given the consequences, it made actuarial sense to see if any impact was on the cards, and at the time no one was troubling themselves to look.
Asteroid strikes were an extreme example of the world’s wilful ignorance, perhaps—but not an atypical one. Low-probability, high-impact events are a fact of life. Individual humans look for protection from them to governments and, if they can afford it, insurers. Humanity, at least as represented by the world’s governments, reveals instead a preference to ignore them until forced to react—even when foresight’s price-tag is small. It is an abdication of responsibility and a betrayal of the future.
Covid-19 offers a tragic example. Virologists, epidemiologists and ecologists have warned for decades of the dangers of a flu-like disease spilling over from wild animals. But when sars–cov-2 began to spread very few countries had the winning combination of practical plans, the kit those plans required in place and the bureaucratic capacity to enact them. Those that did benefited greatly. Taiwan has, to date, seen just seven covid-19 deaths; its economy has suffered correspondingly less.
Pandemics are disasters that governments have experience of. What therefore of truly novel threats? The blazing hot corona which envelops the Sun—seen to spectacular effect during solar eclipses—intermittently throws vast sheets of charged particles out into space. These cause the Northern and Southern Lights and can mess up electric grids and communications. But over the century or so in which electricity has become crucial to much of human life, the Earth has never been hit by the largest of these solar eructations. If a coronal mass ejection (cme) were to hit, all sorts of satellite systems needed for navigation, communications and warnings of missile attacks would be at risk. Large parts of the planet could face months or even years without reliable grid electricity (see Briefing). The chances of such a disaster this century are put by some at better than 50:50. Even if they are not that high, they are still higher than the chances of a national leader knowing who in their government is charged with thinking about such things.
The fact that no governments have ever seen a really big cme, or a volcanic eruption large enough to affect harvests around the world—the most recent was Tambora, in 1815—may explain their lack of forethought. It does not excuse it. Keeping an eye on the future is part of what governments are for. Scientists have provided them with the tools for such efforts, but few academics will undertake the work unbidden, unfunded and unsung. Private business may take some steps when it perceives specific risks, but it will not put together plans for society at large….(More)”.
Report for the European Parliament: “A vast range of AI applications are being implemented by European industry, which can be broadly grouped into two categories: i) applications that enhance the performance and efficiency of processes through mechanisms such as intelligent monitoring, optimisation and control; and ii) applications that enhance human-machine collaboration.
At present, such applications are being implemented across a broad range of European industrial sectors. However, some sectors (e.g. automotive, telecommunications, healthcare) are more advanced in AI deployment than others (e.g. paper and pulp, pumps, chemicals). The types of AI applications
implemented also differ across industries. In less digitally mature sectors, clear barriers to adoption have been identified, including both internal (e.g. cultural resistance, lack of skills, financial considerations) and external (e.g. lack of venture capital) barriers. For the most part, and especially for SMEs, barriers to the adoption of AI are similar to those hindering digitalisation. The adoption of such AI applications is anticipated to deliver a wide range of positive impacts, for individual firms, across value chains, as well as at the societal and macroeconomic levels. AI applications can bring efficiency, environmental and economic benefits related to increased production output and quality, reduced maintenance costs, improved energy efficiency, better use of raw materials and reduced waste. In addition, AI applications can add value through product personalisation, improve customer service and contribute to the development of new product classes, business models and even sectors. Workforce benefits (e.g. improved workplace safety) are also being delivered by AI applications.
Alongside these firm-level benefits and opportunities, significant positive societal and economy-wide impacts are envisaged. More specifically, substantial increases in productivity, innovation, growth and job creation have been forecasted. For example, one estimate anticipates labour productivity increases of 11-37% by 2035. In addition, AI is expected to positively contribute to the UN Sustainable Development Goals and the capabilities of AI and machine learning to address major health challenges, such as the current COVID-19 health pandemic, are also noteworthy. For instance, AI systems have the potential to accelerate the lead times for the development of vaccines and drugs.
However, AI adoption brings a range of challenges…(More)”.
Article by Jon Simonsson, Chair of the Committee for Technological Innovation and Ethics (Komet) in Sweden: “People have said that in the present – the fourth industrial revolution – everything is possible. The ingredients are there – 5G, IoT, AI, drones and self-driving vehicles – as well as advanced knowledge about diagnosis and medication – and they are all rapidly evolving. Only the innovator sets the limitations for how to mix and bake with Technologies.
And right now, when the threat of the corona virus has almost shock-digitized both business and the public sector, the interest in new technology solutions has skyrocketed. Working remotely, moving things without human presence, or – most important – virus vaccines and medical treatment methods, have all become self-evident areas for intensified research and experimentation. But the laws and regulations surrounding these areas were often created for a completely different setting.
Rules are good. And there are usually very good reasons why an area is regulated. Some rules are intended to safeguard democratic rights or individual rights to privacy, others to control developments in a certain direction. The rules are required. Especially at the present when not only development of technology but also the technology uptake in society is accelerating. It takes time to develop laws and regulations, and the process of doing so is not in pace with the rapid development of technology. This creates risks in society. For example, risks related to the individual’s right to privacy, the economy or the environment. At the same time, gaps in regulation may be revealed, gaps that could lead to introduction of new and perhaps not desired solutions.
Would it be possible to find a middle ground and a more future oriented way to work with regulation? With rules that are clear, future-proof and developed with legally safe methods, but encourages and facilitates ethical and sustainable innovation?
Responsible development and use of new technology
The Government wants Sweden to be a leader in the responsible development and use of new technologies. The Swedish Committee for Technological Innovation and Ethics (Komet) works with policy development to create good conditions for innovation and competitiveness, while ensuring that development and dissemination of new technology is safe and secure. The Committee helps the Swedish government to proactively address improvements technology could create for citizens, business and society, but also to highlight the conflicting goals that may arise.
This includes raising ethical issues related to the rapid technological development. When almost everything is possible, we need to place particularly high demands on the compass, how we responsibly navigate the technology landscape. Not least during the corona pandemic, when we have seen how ethical boundaries have been moved for the use of surveillance technology.
An important objective of the Komet work is to instil courage in the public sector. Although innovators are often private, at the end of the day, it is the public sector that must enable, be willing to and dare to meet the demands of both business and society. It is the public sector’s role to ensure that the proper regulations are on the table. A balanced and future-oriented regulation which will be required for rapidly creating a sustainable world….(More)”.