Stefaan Verhulst
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)”.
Book by David Stasavage: “Historical accounts of democracy’s rise tend to focus on ancient Greece and pre-Renaissance Europe. The Decline and Rise of Democracy draws from global evidence to show that the story is much richer—democratic practices were present in many places, at many other times, from the Americas before European conquest, to ancient Mesopotamia, to precolonial Africa. Delving into the prevalence of early democracy throughout the world, David Stasavage makes the case that understanding how and where these democracies flourished—and when and why they declined—can provide crucial information not just about the history of governance, but also about the ways modern democracies work and where they could manifest in the future.
Drawing from examples spanning several millennia, Stasavage first considers why states developed either democratic or autocratic styles of governance and argues that early democracy tended to develop in small places with a weak state and, counterintuitively, simple technologies. When central state institutions (such as a tax bureaucracy) were absent—as in medieval Europe—rulers needed consent from their populace to govern. When central institutions were strong—as in China or the Middle East—consent was less necessary and autocracy more likely. He then explores the transition from early to modern democracy, which first took shape in England and then the United States, illustrating that modern democracy arose as an effort to combine popular control with a strong state over a large territory. Democracy has been an experiment that has unfolded over time and across the world—and its transformation is ongoing.
Amidst rising democratic anxieties, The Decline and Rise of Democracy widens the historical lens on the growth of political institutions and offers surprising lessons for all who care about governance….(More)”.
Article by Simine Vazire: “THE RUSH FOR scientific cures and treatments for Covid-19 has opened the floodgates of direct communication between scientists and the public. Instead of waiting for their work to go through the slow process of peer review at scientific journals, scientists are now often going straight to print themselves, posting write-ups of their work to public servers as soon as they’re complete. This disregard for the traditional gatekeepers has led to grave concerns among both scientists and commentators: Might not shoddy science—and dangerous scientific errors—make its way into the media, and spread before an author’s fellow experts can correct it? As two journalism professors suggested in an op-ed last month for The New York Times, it’s possible the recent spread of so-called preprints has only “sown confusion and discord with a general public not accustomed to the high level of uncertainty inherent in science.”
There’s another way to think about this development, however. Instead of showing (once again) that formal peer review is vital for good science, the last few months could just as well suggest the opposite. To me, at least—someone who’s served as an editor at seven different journals, and editor in chief at two—the recent spate of decisions to bypass traditional peer review gives the lie to a pair of myths that researchers have encouraged the public to believe for years: First, that peer-reviewed journals publish only trustworthy science; and second, that trustworthy science is published only in peer-reviewed journals.
Scientists allowed these myths to spread because it was convenient for us. Peer-reviewed journals came into existence largely to keep government regulators off our backs. Scientists believe that we are the best judges of the validity of each other’s work. That’s very likely true, but it’s a huge leap from that to “peer-reviewed journals publish only good science.” The most selective journals still allow flawed studies—even really terribly flawed ones—to be published all the time. Earlier this month, for instance, the journal Proceedings of the National Academy of Sciences put out a paper claiming that mandated face coverings are “the determinant in shaping the trends of the pandemic.” PNAS is a very prestigious journal, and their website claims that they are an “authoritative source” that works “to publish only the highest quality scientific research.” However, this paper was quickly and thoroughly criticized on social media; by last Thursday, 45 researchers had signed a letter formally calling for its retraction.
Now the jig is up. Scientists are writing papers that they want to share as quickly as possible, without waiting the months or sometimes years it takes to go through journal peer review. So they’re ditching the pretense that journals are a sure-fire quality control filter, and sharing their papers as self-published PDFs. This might be just the shakeup that peer review needs….(More)”.
Federica Carugati at Wired: “…A new report by OpenAI suggests we should create external auditing bodies to evaluate the societal impact of algorithm-based decisions. But the report does not specify what such bodies should look like.
We don’t know how to regulate algorithms, because their application to societal problems involves a fundamental incongruity. Algorithms follow logical rules in order to optimize for a given outcome. Public policy is all a matter of trade-offs: optimizing for some groups in society necessarily makes others worse off.
Resolving social trade-offs requires that many different voices be heard. This may sound radical, but it is in fact the original lesson of democracy: Citizens should have a say. We don’t know how to regulate algorithms, because we have become shockingly bad at citizen governance.
Is citizen governance feasible today? Sure, it is. We know from social scientists that a diverse group of people can make very good decisions. We also know from a number of recent experiments that citizens can be called upon to make decisions on very tough policy issues, including climate change, and even to shape constitutions. Finally, we can draw from the past for inspiration on how to actually build citizen-run institutions.
The ancient Athenians—the citizens of the world’s first large-scale experiment in democracy—built an entire society on the principle of citizen governance. One institution stands out for our purposes: the Council of Five Hundred, a deliberative body in charge of all decisionmaking, from war to state finance to entertainment. Every year, 50 citizens from each of the 10 tribes were selected by lot to serve. Selection occurred among those that had not served the year before and had not already served twice.
These simple organizational rules facilitated broad participation, knowledge aggregation, and citizen learning. First, because the term was limited and could not be iterated more than twice, over time a broad section of the population—rich and poor, educated and not—participated in decisionmaking. Second, because the council represented the whole population (each tribe integrated three different geographic constituencies), it could draw upon the diverse knowledge of its members. Third, at the end of their mandate, councillors returned home with a body of knowledge about the affairs of their city that they could share with their families, friends, and coworkers, some of whom already served and some who soon would. Certainly, the Athenians did not follow through on their commitment to inclusion. As a result, many people’s voices went unheard, including those of women, foreigners, and slaves. But we don’t need to follow the Athenian example on this front.
A citizen council for algorithms modeled on the Athenian example would represent the entire American citizen population. We already do this with juries (although it is possible that, when decisions affect a specific constituency, a better fit with the actual polity might be required). Citizens’ deliberations would be informed by agency self-assessments and algorithmic impact statements for decision systems used by government agencies, and internal auditing reports for industry, as well as reports from investigative journalists and civil society activists, whenever available. Ideally, the council would act as an authoritative body or as an advisory board to an existing regulatory agency….(More)”.