Realtime Climate

Curated on Posted on by Stefaan Verhulst

Climate Central …:”launched this tool to help meteorologists and journalists cover connections between weather, news, and climate in real time, and to alert public and private organizations and individuals about particular local conditions related to climate change, its impacts, or its solutions.

Realtime Climate monitors local weather and events across the U.S. and generates alerts when certain conditions are met or expected. These alerts provide links to science-based analyses and visualizations—including locality-specific, high-quality graphics—that can help explain events in the context of climate change….

Alerts are sent when particular conditions occur or are forecast to occur in the next few days. Examples include:

  • Unusual heat (single day and multi-day)
  • Heat Index
  • Unusual Rainfall
  • Coastal Flooding
  • Air Quality
  • Allergies
  • Seasonal shifts (spring leaf-out, etc.)
  • Ice/snow cover (Great Lakes)
  • Cicadas
  • High local or regional production of solar or wind energy

More conditions will be added soon, including:

  • Drought
  • Wildfire
  • and many more…(More)”.

Engaging with the public about algorithmic transparency in the public sector

Curated on Posted on by Stefaan Verhulst

Blog by the Centre for Data Ethics and Innovation (UK): “To move the recommendation that we made in our review into bias in algorithmic decision-making forward, we have been working with the Central Digital and Data Office (CDDO) and BritainThinks to scope what a transparency obligation could look like in practice, and in particular, which transparency measures would be most effective at increasing public understanding about the use of algorithms in the public sector. 

Due to the low levels of awareness about the use of algorithms in the public sector (CDEI polling in July 2020 found that 38% of the public were not aware that algorithmic systems were used to support decisions using personal data), we opted for a deliberative public engagement approach. This involved spending time gradually building up participants’ understanding and knowledge about algorithm use in the public sector and discussing their expectations for transparency, and co-designing solutions together. 

For this project, we worked with a diverse range of 36 members of the UK public, spending over five hours engaging with them over a three week period. We focused on three particular use-cases to test a range of emotive responses – policing, parking and recruitment.  

The final stage was an in-depth co-design session, where participants worked collaboratively to review and iterate prototypes in order to develop a practical approach to transparency that reflected their expectations and needs for greater openness in the public sector use of algorithms. 

What did we find? 

Our research validated that there was fairly low awareness or understanding of the use of algorithms in the public sector. Algorithmic transparency in the public sector was not a front-of-mind topic for most participants.

However, once participants were introduced to specific examples of potential public sector algorithms, they felt strongly that transparency information should be made available to the public, both citizens and experts. This included desires for; a description of the algorithm, why an algorithm was being used, contact details for more information, data used, human oversight, potential risks and technicalities of the algorithm…(More)”.

Who’s Afraid of Big Numbers?

Curated on Posted on by Stefaan Verhulst

Aiyana Green and Steven Strogatz at the New York Times: “Billions” and “trillions” seem to be an inescapable part of our conversations these days, whether the subject is Jeff Bezos’s net worth or President Biden’s proposed budget. Yet nearly everyone has trouble making sense of such big numbers. Is there any way to get a feel for them? As it turns out, there is. If we can relate big numbers to something familiar, they start to feel much more tangible, almost palpable.

For example, consider Senator Bernie Sanders’s signature reference to “millionaires and billionaires.” Politics aside, are these levels of wealth really comparable? Intellectually, we all know that billionaires have a lot more money than millionaires do, but intuitively it’s hard to feel the difference, because most of us haven’t experienced what it’s like to have that much money.

In contrast, everyone knows what the passage of time feels like. So consider how long it would take for a million seconds to tick by. Do the math, and you’ll find that a million seconds is about 12 days. And a billion seconds? That’s about 32 years. Suddenly the vastness of the gulf between a million and a billion becomes obvious. A million seconds is a brief vacation; a billion seconds is a major fraction of a lifetime.

Comparisons to ordinary distances provide another way to make sense of big numbers. Here in Ithaca, we have a scale model of the solar system known as the Sagan Walk, in which all the planets and the gaps between them are reduced by a factor of five billion. At that scale, the sun becomes the size of a serving plate, Earth is a small pea and Jupiter is a brussels sprout. To walk from Earth to the sun takes just a few dozen footsteps, whereas Pluto is a 15-minute hike across town. Strolling through the solar system, you gain a visceral understanding of astronomical distances that you don’t get from looking at a book or visiting a planetarium. Your body grasps it even if your mind cannot….(More)”.

Governance mechanisms for sharing of health data: An approach towards selecting attributes for complex discrete choice experiment studies

Curated on Posted on by Stefaan Verhulst

Paper by Jennifer Viberg Johansson: “Discrete Choice Experiment (DCE) is a well-established technique to elicit individual preferences, but it has rarely been used to elicit governance preferences for health data sharing.

The aim of this article was to describe the process of identifying attributes for a DCE study aiming to elicit preferences of citizens in Sweden, Iceland and the UK for governance mechanisms for digitally sharing different kinds of health data in different contexts.

A three-step approach was utilised to inform the attribute and level selection: 1) Attribute identification, 2) Attribute development and 3) Attribute refinement. First, we developed an initial set of potential attributes from a literature review and a workshop with experts. To further develop attributes, focus group discussions with citizens (n = 13), ranking exercises among focus group participants (n = 48) and expert interviews (n = 18) were performed. Thereafter, attributes were refined using group discussion (n = 3) with experts as well as cognitive interviews with citizens (n = 11).

The results led to the selection of seven attributes for further development: 1) level of identification, 2) the purpose of data use, 3) type of information, 4) consent, 5) new data user, 6) collector and 7) the oversight of data sharing. Differences were found between countries regarding the order of top three attributes. The process outlined participants’ conceptualisation of the chosen attributes, and what we learned for our attribute development phase.

This study demonstrates a process for selection of attributes for a (multi-country) DCE involving three stages: Attribute identification, Attribute development and Attribute refinement. This study can contribute to improve the ethical aspects and good practice of this phase in DCE studies. Specifically, it can contribute to the development of governance mechanisms in the digital world, where people’s health data are shared for multiple purposes….(More)”.

Moving up: Promoting workers’ upward mobility using network analysis

Curated on Posted on by Stefaan Verhulst

Report by Marcela Escobari, Ian Seyal and Carlos Daboin Contreras: “The U.S. economy faces a mobility crisis. After decades of rising inequality, stagnating wages, and a shrinking middle class, many American workers find it harder and harder to get ahead. COVID-19 accentuated a stark divide, battering a two-tiered labor force with millions of low-wage workers lacking job security and benefits—as the long-term trends of globalization, digitalization, and automation continue to displace jobs and disrupt career paths.

To address this crisis and create an economy that works for everyone, policymakers and business leaders must act boldly and urgently. But the challenge of low mobility is complex and driven by many factors, with significant heterogeneity across regions, sectors, and demographic groups. When diagnostics fail to disentangle the complexity, our standard policy responses—centered on education, reskilling, and other reemployment services to help workers adapt—fall short.

This report offers a new approach to better understand the contours of mobility: Who is falling behind, where, and by how much. Using data on hundreds of thousands of real workers’ occupational transitions, we use network analysis to create a multidimensional map of the labor market, revealing a landscape riddled with mobility gaps and barriers. Workers in low-wage occupations face particular hurdles, and persistent racial and gender disparities hold some workers back more than others.

Even so, many workers travel on pathways to economic mobility. By showing where existing pathways can be expanded and where new ones are needed, this report helps policymakers, community organizations, higher education institutions, and business leaders better understand the challenge of mobility and see where and how to intervene, in order to help more workers move up faster….(More)”.

When Graphs Are a Matter of Life and Death

Curated on Posted on by Stefaan Verhulst

Essay by  Hannah Fry at the NewYorker: “John Carter has only an hour to decide. The most important auto race of the season is looming; it will be broadcast live on national television and could bring major prize money. If his team wins, it will get a sponsorship deal and a chance to start making some real profits for a change.

There’s just one problem. In seven of the past twenty-four races, the engine in the Carter Racing car has blown out. An engine failure live on TV will jeopardize sponsorships—and the driver’s life. But withdrawing has consequences, too. The wasted entry fee means finishing the season in debt, and the team won’t be happy about the missed opportunity for glory. As Burns’s First Law of Racing says, “Nobody ever won a race sitting in the pits.”

One of the engine mechanics has a hunch about what’s causing the blowouts. He thinks that the engine’s head gasket might be breaking in cooler weather. To help Carter decide what to do, a graph is devised that shows the conditions during each of the blowouts: the outdoor temperature at the time of the race plotted against the number of breaks in the head gasket. The dots are scattered into a sort of crooked smile across a range of temperatures from about fifty-five degrees to seventy-five degrees.

When Graphs Are a Matter of Life and Death

The upcoming race is forecast to be especially cold, just forty degrees, well below anything the cars have experienced before. So: race or withdraw?

This case study, based on real data, and devised by a pair of clever business professors, has been shown to students around the world for more than three decades. Most groups presented with the Carter Racing story look at the scattered dots on the graph and decide that the relationship between temperature and engine failure is inconclusive. Almost everyone chooses to race. Almost no one looks at that chart and asks to see the seventeen missing data points—the data from those races which did not end in engine failure.

Image may contain Plot

As soon as those points are added, however, the terrible risk of a cold race becomes clear. Every race in which the engine behaved properly was conducted when the temperature was higher than sixty-five degrees; every single attempt that occurred in temperatures at or below sixty-five degrees resulted in engine failure. Tomorrow’s race would almost certainly end in catastrophe.

One more twist: the points on the graph are real but have nothing to do with auto racing. The first graph contains data compiled the evening before the disastrous launch of the space shuttle Challenger, in 1986….(More)”.

Cultivating an Inclusive Culture Through Personal Networks

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Essay by Rob Cross, Kevin Oakes, and Connor Cross: “Many organizations have ramped up their investments in diversity, equity, and inclusion — largely in the form of anti-bias training, employee resource groups, mentoring programs, and added DEI functions and roles. But gauging the effectiveness of these measures has been a challenge….

We’re finding that organizations can get a clearer picture of employee experience by analyzing people’s network connections. They can begin to see whether DEI programs are producing the collaboration and interactions needed to help people from various demographic groups gain their footing quickly and become truly integrated.

In particular, network analysis reveals when and why people seek out individuals for information, ideas, career advice, personal support, or mentorship. In the Connected Commons, a research consortium, we have mapped organizational networks for over 20 years and have frequently been able to overlay gender data on network diagrams to identify drivers of inclusion. Extensive quantitative and qualitative research on this front has helped us understand behaviors that promote more rapid and effective integration of women after they are hired. For example, research reveals the importance of fostering collaboration across functional and geographic divides (while avoiding collaborative burnout) and cultivating energy through network connections….(More)”

Examining the Intersection of Behavioral Science and Advocacy

Curated on Posted on by Stefaan Verhulst

Introduction to Special Collection of the Behavioral Scientist by Cintia Hinojosa and Evan Nesterak: “Over the past year, everyone’s lives have been touched by issues that intersect science and advocacy—the pandemic, climate change, police violence, voting, protests, the list goes on. 

These issues compel us, as a society and individuals, toward understanding. We collect new data, design experiments, test our theories. They also inspire us to examine our personal beliefs and values, our roles and responsibilities as individuals within society. 

Perhaps no one feels these forces more than social and behavioral scientists. As members of fields dedicated to the study of social and behavioral phenomena, they are in the unique position of understanding these issues from a scientific perspective, while also navigating their inevitable personal impact. This dynamic brings up questions about the role of scientists in a changing world. To what extent should they engage in advocacy or activism on social and political issues? Should they be impartial investigators, active advocates, something in between? 

t also raises other questions, like does taking a public stance on an issue affect scientific integrity? How should scientists interact with those setting policies? What happens when the lines between an evidence-based stance and a political position become blurred? What should scientists do when science itself becomes a partisan issue? 

To learn more about how social and behavioral scientists are navigating this terrain, we put out a call inviting them to share their ideas, observations, personal reflections, and the questions they’re grappling with. We gave them 100-250 words to share what was on their mind. Not easy for such a complex and consequential topic.

The responses, collected and curated below, revealed a number of themes, which we’ve organized into two parts….(More)”.

Is It Time for a U.S. Department of Science?

Curated on Posted on by Stefaan Verhulst


Essay by Anthony Mills: “The Biden administration made history earlier this year by elevating the director of the Office of Science and Technology Policy to a cabinet-level post. There have long been science advisory bodies within the White House, and there are a number of executive agencies that deal with science, some of them cabinet-level. But this will be the first time in U.S. history that the president’s science advisor will be part of his cabinet.

It is a welcome effort to restore the integrity of science, at a moment when science has been thrust onto the center-stage of public life — as something indispensable to political decision-making as well as a source of controversy and distrust. Some have urged the administration to go even further, calling for the creation of a new federal department of science. Such calls to centralize science have a long history, and have grown louder during the coronavirus pandemic, spurred by our government’s haphazard response.

But more centralization is not the way to restore the integrity of science. Centralization has its place, especially during national emergencies. Too much of it, however, is bad for science. As a rule, science flourishes in a decentralized research environment, which balances the need for public support, effective organization, and political accountability with scientific independence and institutional diversity. The Biden administration’s move is welcome. But there is risk in what it could lead to next: an American Ministry of Science. And there is an opportunity to create a needed alternative….(More)”.

Tasks, Automation, and the Rise in US Wage Inequality

Curated on Posted on by Stefaan Verhulst

Paper by Daron Acemoglu & Pascual Restrepo: “We document that between 50% and 70% of changes in the US wage structure over the last four decades are accounted for by the relative wage declines of worker groups specialized in routine tasks in industries experiencing rapid automation. We develop a conceptual framework where tasks across a number of industries are allocated to different types of labor and capital. Automation technologies expand the set of tasks performed by capital, displacing certain worker groups from employment opportunities for which they have comparative advantage. This framework yields a simple equation linking wage changes of a demographic group to the task displacement it experiences.

We report robust evidence in favor of this relationship and show that regression models incorporating task displacement explain much of the changes in education differentials between 1980 and 2016. Our task displacement variable captures the effects of automation technologies (and to a lesser degree offshoring) rather than those of rising market power, markups or deunionization, which themselves do not appear to play a major role in US wage inequality. We also propose a methodology for evaluating the full general equilibrium effects of task displacement (which include induced changes in industry composition and ripple effects as tasks are reallocated across different groups). Our quantitative evaluation based on this methodology explains how major changes in wage inequality can go hand-in-hand with modest productivity gains….(More)”.