Stefaan Verhulst
Dark Matter Laboratories: “…As with all so-called wicked problems, the climate crisis occurs at the intersection of human and natural systems, where interdependent components interact at multiple scales causing uncertainty and emergent, erratic fluctuations. Interventions in such systems can trigger disproportionate impacts in other areas due to feedback effects. On top of this, collective action problems, such as identifying and implementing climate crisis adaptation or mitigation strategies, involve trade-offs and conflicting motivations between the different decision-makers. All of this presents challenges when identifying solutions, or even agreeing on a shared definition of the problem.
As is often the case in times of crisis, collective community-led actions have been a vital part of the response to the COVID-19 pandemic. Communities have demonstrated their capacity to mobilise efficiently in areas where the public sector has been either too slow, unable, or unwilling to intervene. Yet, the pandemic has also put into perspective the scale of response required to address the climate crisis. Despite a near-total shutdown of the global economy, annual CO2 emissions are only expected to fall by 5.6% this year, falling short of the 7.6% target required to ensure a temperature rise of no more than 1.5°C. Can AI help amplify and coordinate collective action to the scale necessary for effective climate crisis response? In this post, we explore alternative futures that leverage the significant potential of citizen groups to act at a local level in order to achieve global impact.
Applying AI to climate problems
There are various research collaborations, open challenges, and corporate-led initiatives that already exist in the field of AI and climate crisis. Climate Change AI, for instance, has identified a range of opportunity domains for a selection of machine learning (ML) methods. These applications range from electrical systems and transportation to collective decisions and education. Google.org’s Impact Challenge supports initiatives applying AI for social good, while the AI for Good platform aims to identify practical applications of AI that can be scaled for global impact. These initiatives and many others, such as Project Drawdown, have informed our research into opportunity areas for AI to augment Collective Intelligence.
Throughout the project, we have been wary that attempts to apply AI to complex problems can suffer from technological solutionism, which loses sight of the underlying issues. To try to avoid this, with Civic AI, we have focused on understanding community challenges before identifying which parts of the problem are most suited to AI’s strengths, especially as this is just one of the many tools available. Below, we explore how AI could be used to complement and enhance community-led efforts as part of inclusive civic infrastructures.
We define civic assets as the essential shared infrastructure that benefits communities such as an urban forest or a community library. We will explore their role in climate crisis mitigation and adaptation. What does a future look like in which these assets are semi-autonomous and highly participatory, fostering collaboration between people and machines?…(More) –
Report by Miguel Pereira and Patrik Öhberg: “We argue that policy expertise may constrain the ability of politicians to be responsive. Legislators with more knowledge and experience in a given policy area have more confidence in their own issue-specific positions. Enhanced confidence, in turn, may lead legislators to discount opinions they disagree with. Two experiments with Swedish politicians support our argument. First, we find that officials with more expertise in a given domain are more likely to dismiss appeals from voters who hold contrasting opinions, regardless of their specific position on the policy, and less likely to accept that opposing views may represent the majority opinion. Consistent with the proposed mechanism, in a second experiment we show that inducing perceptions of expertise increases self-confidence. The results suggest that representatives with more expertise in a given area are paradoxically less capable of voicing public preferences in that domain. The study provides a novel explanation for distortions in policy responsiveness….(More)”
Article by Alex Chesterfield and Kate Coombs: “…One reason for this effect, and for the polarizing outcome, is we often overestimate our understanding of how political policies work. In this case, the more omniscient we think we are, the easier it is to ignore alternative facts or ideas. This phenomenon has a name—the illusion of explanatory depth (IOED). Unless explicitly tested, individuals can remain largely unaware of the shallowness of their own understanding of the things they think they understand—such as the mechanics of a bicycle, or how the policy they support or despise will actually work.
Researchers have started to explore what happens to political attitudes when you explicitly test people on how much they actually know about a policy. When people discover that they don’t know as much as they thought they did, something interesting happens: their political attitudes become less extreme….
Some countries and institutions are already using these insights to improve decision-making on divisive topics. Deliberative democracy, which plays out in the form of citizens’ assemblies and juries, where a small group of people (12-24) come together to deliberate on an issue, provide time and information to encourage participants to generate explanations—rather than justifications based on values, hearsay, or feelings—for their positions. Participants also tend to be representative of the general population; research suggests that increasing contact between diverse individuals could also help diminish affective polarization by shrinking the prejudices we form when making assumptions about the “other” that are based on reductive stereotypes, rather than real, complex people.
Outside of juries and citizens assemblies, countries like Ireland have used deliberative democracy to address a range of complex and highly polarized issues including same-sex marriage, access to abortion, and climate change. U.K. politicians from both sides of the aisle have called for a Brexit assembly to try and break the U.K. political deadlock. Will it work? We don’t know yet, and we’d encourage researchers to continue to study this topic. In the meantime, we can each begin by confronting our own ignorance. Before committing to a position or policy, ask yourself to explain mechanistically how you think it will bring about the intended outcome. Do you really understand it?
Test your own mechanistic reasoning. Pick a topic you feel strongly about: climate change, Brexit, Immigration, gun laws, assisted suicide/legal euthanasia. Instead of justifying why you support a particular position so strongly, try to explain how it might lead to a particular outcome….(More)”
Liv Grjebine at Scientific American: “The confidence people place in science is frequently based not on what it really is, but on what people would like it to be. When I asked students at the beginning of the year how they would define science, many of them replied that it is an objective way of discovering certainties about the world. But science cannot provide certainties. For example, a majority of Americans trust science as long as it does not challenge their existing beliefs. To the question “When science disagrees with the teachings of your religion, which one do you believe?,” 58 percent of North Americans favor religion; 33 percent science; and 6 percent say “it depends.”
But doubt in science is a feature, not a bug. Indeed, the paradox is that science, when properly functioning, questions accepted facts and yields both new knowledge and new questions—not certainty. Doubt does not create trust, nor does it help public understanding. So why should people trust a process that seems to require a troublesome state of uncertainty without always providing solid solutions?
As a historian of science, I would argue that it’s the responsibility of scientists and historians of science to show that the real power of science lies precisely in what is often perceived as its weakness: its drive to question and challenge a hypothesis. Indeed, the scientific approach requires changing our understanding of the natural world whenever new evidence emerges from either experimentation or observation. Scientific findings are hypotheses that encompass the state of knowledge at a given moment. In the long run, many of are challenged and even overturned. Doubt might be troubling, but it impels us towards a better understanding; certainties, as reassuring as they may seem, in fact undermine the scientific process….(More)”.
Paper by Jennifer Allen, Baird Howland, Markus Mobius, David Rothschild and Duncan J. Watts: “Fake news,” broadly defined as false or misleading information masquerading as legitimate news, is frequently asserted to be pervasive online with serious consequences for democracy. Using a unique multimode dataset that comprises a nationally representative sample of mobile, desktop, and television consumption, we refute this conventional wisdom on three levels. First, news consumption of any sort is heavily outweighed by other forms of media consumption, comprising at most 14.2% of Americans’ daily media diets. Second, to the extent that Americans do consume news, it is overwhelmingly from television, which accounts for roughly five times as much as news consumption as online. Third, fake news comprises only 0.15% of Americans’ daily media diet. Our results suggest that the origins of public misinformedness and polarization are more likely to lie in the content of ordinary news or the avoidance of news altogether as they are in overt fakery….(More)”.
Paper by Alissa Fishbane, Aurelie Ouss and Anuj K. Shah: “Each year, millions of Americans fail to appear in court for low-level offenses, and warrants are then issued for their arrest. In two field studies in New York City, we make critical information salient by redesigning the summons form and providing text message reminders. These interventions reduce failures to appear by 13-21% and lead to 30,000 fewer arrest warrants over a 3-year period. In lab experiments, we find that while criminal justice professionals see failures to appear as relatively unintentional, laypeople believe they are more intentional. These lay beliefs reduce support for policies that make court information salient and increase support for punishment. Our findings suggest that criminal justice policies can be made more effective and humane by anticipating human error in unintentional offenses….(More)”
UCL Institute for Innovation and Public Purpose (IIPP) Working Paper: “The market size and strength of the major digital platform companies has invited international concern about how such firms should best be regulated to serve the interests of wider society, with a particular emphasis on the need for new anti-trust legislation. Using a normative innovation systems approach, this paper investigates how current anti-trust models may insufficiently address the value-extracting features of existing data-intensive and platform-oriented industry behaviour and business models. To do so, we employ the concept of economic rents to investigate how digital platforms create and extract value. Two forms of rent are elaborated: ‘network monopoly rents’ and ‘algorithmic rents’. By identifying such rents more precisely, policymakers and researchers can better direct regulatory investigations, as well as broader industrial and innovation policy approaches, to shape the features of platform-driven digital markets…(More)”.
Book by Tim Harford: “When was the last time you read a grand statement, accompanied by a large number, and wondered whether it could really be true? Statistics are vital in helping us tell stories – we see them in the papers, on social media, and we hear them used in everyday conversation – and yet we doubt them more than ever.
But numbers – in the right hands – have the power to change the world for the better. Contrary to popular belief, good statistics are not a trick, although they are a kind of magic. Good statistics are not smoke and mirrors; in fact, they help us see more clearly. Good statistics are like a telescope for an astronomer, a microscope for a bacteriologist, or an X-ray for a radiologist. If we are willing to let them, good statistics help us see things about the world around us and about ourselves – both large and small – that we would not be able to see in any other way.
In How to Make the World Add Up, Tim Harford draws on his experience as both an economist and presenter of the BBC’s radio show ‘More or Less’. He takes us deep into the world of disinformation and obfuscation, bad research and misplaced motivation to find those priceless jewels of data and analysis that make communicating with numbers worthwhile. Harford’s characters range from the art forger who conned the Nazis to the stripper who fell in love with the most powerful congressman in Washington, to famous data detectives such as John Maynard Keynes, Daniel Kahneman and Florence Nightingale. He reveals how we can evaluate the claims that surround us with confidence, curiosity and a healthy level of scepticism.
Using ten simple rules for understanding numbers – plus one golden rule – this extraordinarily insightful book shows how if we keep our wits about us, thinking carefully about the way numbers are sourced and presented, we can look around us and see with crystal clarity how the world adds up….(More)”.
Data Collaborative Case Study by Michelle Winowatan, Andrew J. Zahuranec, Andrew Young, and Stefaan Verhulst: “Following the 2015 earthquake in Nepal, Flowminder, a data analytics nonprofit, and NCell, a mobile operator in Nepal, formed a data collaborative. Using call detail records (CDR, a type of mobile operator data) provided by NCell, Flowminder estimated the number of people displaced by the earthquake and their location. The result of the analysis was provided to various humanitarian agencies responding to the crisis in Nepal to make humanitarian aid delivery more efficient and targeted.
Data Collaboratives Model: Based on our typology of data collaborative practice areas, the initiative follows the trusted intermediary model of data collaboration, specifically a third-party analytics approach. Third-party analytics projects involve trusted intermediaries — such as Flowminder — who access private-sector data, conduct targeted analysis, and share insights with public or civil sector partners without sharing the underlying data. This approach enables public interest uses of private-sector data while retaining strict access control. It brings outside data expertise that would likely not be available otherwise using direct bilateral collaboration between data holders and users….(More)”.
Brendan Foht at New Atlantis: “If there is one thing about the coronavirus pandemic that both sides of the political spectrum seem to agree on, it’s that the science that bears on it should never be “politicized.” From the left, former CDC directors of the Obama and Clinton administrations warn of how the Trump administration has politicized the agency’s science: “The only valid reason to change released guidelines is new information and new science — not politics.” From the right, the Wall Street Journal frets about the scientific journal Nature publishing a politically charged editorial about why China shouldn’t be blamed for the coronavirus: “Political pressure has distorted scientific judgment.” What both sides assume is that political authorities should defer to scientists on important decisions about the pandemic, but only insofar as science itself is somehow kept free from politics.
But politicization, and even polarization, are not always bad for science. There is much about how we can use science to respond to the pandemic that is inescapably political, and that we cannot simply leave to scientists to decide.
There is, however, a real problem with how political institutions in the United States have engaged with science. Too much of the debate over coronavirus science has centered on how bad the disease really is, with the administration downplaying its risks and the opposition insisting on its danger. One side sees the scientists warning of peril as a political obstacle that must be overcome. The other side sees them as authorities to whom we must defer, not as servants of the public who could be directed toward solving the problem. The false choice between these two perspectives on how science relates to politics obscures a wide range of political choices the country faces about how we can make use of our scientific resources in responding to the pandemic….(More)”.