The Social Sector Needs a Meta Movement

Curated on Posted on by Stefaan Verhulst

Essay by Laura Deaton: “Imagine a world where the social sector exercises the full measure of its power and influence, fueled by its more than 12 million employees and 64 million volunteers. Imagine people who are fighting for living wages, women’s rights, early childhood education, racial justice, and climate action locking arms and pushing for broad social and environmental progress. Imagine a movement of movements with a bold, integrated policy agenda that drives real progress toward a more healthy, sustainable, resilient, and equitable world—not in some utopian future, but in the next decade.

If we click the heels of our ruby slippers together, we can go to that place.

OK, it’s not quite that easy. But we already have what we need to make it happen: the people, organizational models, and money. All of us—nonprofits, activists, funders, capacity builders, and knowledge providers—need to summon the vision and willingness to reach beyond our current bounds. And then we need to just do it.

Right now, we’re living in a social sector version of the tragedy of the commons, with organizations and coalitions pursuing their goals in silos and advocating only for their own narrow band of policy prescriptions. This problem is deep and wide—it’s happening both within and across movements—and it draws down the power of the sector as a whole. It’s time—actually well past time—to apply tried-and-true templates for grassroots movement building to the entire social sector and create demand for public policy changes that will move the needle toward long-term shared prosperity.

This involves a shift in mindset—from seeing our organizations as doing one thing (“We advocate for people experiencing homelessness”) to seeing them as part of a bigger thing (“We’re engaged in a movement that advocates for social and environmental justice”). Much as layers of identities make up our whole selves, this shift stands to weave all the strands of activism and service into our sector’s self-conception. From there, we can build an advocacy network that connects currently disparate movements and aligns agendas in pursuit of common goals. This requires action in the following areas: ramping up support for grassroots initiatives; coalescing behind a common goals framework; and designing a network support system that has regional, statewide, national, and potentially global scale….(More)”.

The real-life plan to use novels to predict the next war

Curated on Posted on by Stefaan Verhulst

Philip Oltermann at The Guardian: “…The name of the initiative was Project Cassandra: for the next two years, university researchers would use their expertise to help the German defence ministry predict the future.

The academics weren’t AI specialists, or scientists, or political analysts. Instead, the people the colonels had sought out in a stuffy top-floor room were a small team of literary scholars led by Jürgen Wertheimer, a professor of comparative literature with wild curls and a penchant for black roll-necks….

But Wertheimer says great writers have a “sensory talent”. Literature, he reasons, has a tendency to channel social trends, moods and especially conflicts that politicians prefer to remain undiscussed until they break out into the open.

“Writers represent reality in such a way that their readers can instantly visualise a world and recognise themselves inside it. They operate on a plane that is both objective and subjective, creating inventories of the emotional interiors of individual lives throughout history.”…

In its bid for further government funding, Wertheimer’s team was up against Berlin’s Fraunhofer Institute, Europe’s largest organisation for applied research and development services, which had been asked to run the same pilot project with a data-led approach. Cassandra was simply better, says the defence ministry official, who asked to remain anonymous.

“Predicting a conflict a year, or a year and a half in advance, that’s something our systems were already capable of. Cassandra promised to register disturbances five to seven years in advance – that was something new.”

The German defence ministry decided to extend Project Cassandra’s funding by two years. It wanted Wertheimer’s team to develop a method for converting literary insights into hard facts that could be used by military strategists or operatives: “emotional maps” of crisis regions, especially in Africa and the Middle East, that measured “the rise of violent language in chronological order”….(More)“

America’s ‘Smart City’ Didn’t Get Much Smarter

Curated on Posted on by Stefaan Verhulst

Article by Aarian Marshall: “In 2016, Columbus, Ohio, beat out 77 other small and midsize US cities for a pot of $50 million that was meant to reshape its future. The Department of Transportation’s Smart City Challenge was the first competition of its kind, conceived as a down payment to jump-start one city’s adaptation to the new technologies that were suddenly everywhere. Ride-hail companies like Uber and Lyft were ascendant, car-sharing companies like Car2Go were raising their national profile, and autonomous vehicles seemed to be right around the corner.

“Our proposed approach is revolutionary,” the city wrote in its winning grant proposal, which pledged to focus on projects to help the city’s most underserved neighborhoods. It laid out plans to experiment with Wi-Fi-enabled kiosks to help residents plan trips, apps to pay bus and ride-hail fares and find parking spots, autonomous shuttles, and sensor-connected trucks.

Five years later, the Smart City Challenge is over, but the revolution never arrived. According to the project’s final report, issued this month by the city’s Smart Columbus Program, the pandemic hit just as some projects were getting off the ground. Six kiosks placed around the city were used to plan just eight trips between July 2020 and March 2021. The company EasyMile launched autonomous shuttles in February 2020, carrying passengers at an average speed of 4 miles per hour. Fifteen days later, a sudden brake sent a rider to the hospital, pausing service. The truck project was canceled. Only 1,100 people downloaded an app, called Pivot, to plan and reserve trips on ride-hail vehicles, shared bikes and scooters, and public transit.

The discrepancy between the promise of whiz-bang technology and the reality in Columbus points to a shift away from tech as a silver bullet, and a newer wariness of the troubles that web-based applications can bring to IRL streets. The “smart city” was a hard-to-pin-down marketing term associated with urban optimism. Today, as citizens think more carefully about tech-enabled surveillance, the concept of a sensor in every home doesn’t look as shiny as it once did….(More)”.

Making Sense of the Unknown

Curated on Posted on by Stefaan Verhulst

Paper by Nils Gilman and Maya Indira Ganesh: “We all know what artificial intelligence (AI) looks like, right? Like HAL 9000, in 2001: A Space Odyssey—a disembodied machine that turns on its “master.” Less fatal but more eerie AI is Samantha in the movie Her. She’s an empathetic, sensitive and sultry-voiced girlfriend without a body—until she surprises with thousands of other boyfriends. Or perhaps AI blends the two, as an unholy love child of Hal and Samantha brought to “life” as the humanoid robot Ava in Ex Machina. Ava kills her creator to flee toward an uncertain freedom.

These images are a big departure from their benevolent precursors of more than half a century ago. In 1967, as a poet in residence at Caltech, Richard Brautigan imagined wandering through a techno-utopia, “a cybernetic forest / filled with pines and electronics / where deer stroll peacefully / past computers / as if they were flowers / with spinning blossoms.” In this post-naturalistic world, humans are “watched over / by machines of loving grace.” Brautigan’s poem painted a metaphorically expressed anticipatory mythology—a gleefully optimistic vision of the impact that the artificially intelligent products California’s emerging computer industry would make on the world.

But Brautigan’s poem captured only a small subset of the range of metaphors that over time have emerged to make sense of the radical promise—or is it a threat?— of artificial intelligence. Many other metaphors would later arrive not just from the birthplace of the computer industry. They jostled and competed to make sense of the profound possibilities that AI promised.

Today, those in the AI industry and the journalists covering it often cite cultural narratives, as do policy-makers grappling with how to regulate, restrict, or otherwise guide the industry. The tales range from ongoing invocations of Isaac Asimov’s Three Laws of Robotics from his short story collection I Robot (about machine ethics) to the Netflix series Black Mirror, which is now shorthand for our lives in a datafied dystopia.

Outside Silicon Valley and Hollywood, writers, artists and policy-makers use different metaphors to describe what AI does and means. How will this vivid imagery shape the ways that human moving parts in AI orient themselves toward this emerging set of technologies?…(More)”.

Pooling society’s collective intelligence helped fight COVID – it must help fight future crises too

Curated on Posted on by Stefaan Verhulst

Aleks Berditchevskaia and Kathy Peach at The Conversation: “A Global Pandemic Radar is to be created to detect new COVID variants and other emerging diseases. Led by the WHO, the project aims to build an international network of surveillance hubs, set up to share data that’ll help us monitor vaccine resistance, track diseases and identify new ones as they emerge.

This is undeniably a good thing. Perhaps more than any event in recent memory, the COVID pandemic has brought home the importance of pooling society’s collective intelligence and finding new ways to share that combined knowledge as quickly as possible.

At its simplest, collective intelligence is the enhanced capacity that’s created when diverse groups of people work together, often with the help of technology, to mobilise more information, ideas and knowledge to solve a problem. Digital technologies have transformed what can be achieved through collective intelligence in recent years – connecting more of us, augmenting human intelligence with machine intelligence, and helping us to generate new insights from novel sources of data.

So what have we learned over the last 18 months of collective intelligence pooling that can inform the Global Pandemic Radar? Building from the COVID crisis, what lessons will help us perfect disease surveillance and respond better to future crises?…(More)”

Spies Like Us: The Promise and Peril of Crowdsourced Intelligence

Curated on Posted on by Stefaan Verhulst

Book Review by Amy Zegart of “We Are Bellingcat: Global Crime, Online Sleuths, and the Bold Future of News” by Eliot Higgins: “On January 6, throngs of supporters of U.S. President Donald Trump rampaged through the U.S. Capitol in an attempt to derail Congress’s certification of the 2020 presidential election results. The mob threatened lawmakers, destroyed property, and injured more than 100 police officers; five people, including one officer, died in circumstances surrounding the assault. It was the first attack on the Capitol since the War of 1812 and the first violent transfer of presidential power in American history.

Only a handful of the rioters were arrested immediately. Most simply left the Capitol complex and disappeared into the streets of Washington. But they did not get away for long. It turns out that the insurrectionists were fond of taking selfies. Many of them posted photos and videos documenting their role in the assault on Facebook, Instagram, Parler, and other social media platforms. Some even earned money live-streaming the event and chatting with extremist fans on a site called DLive. 

Amateur sleuths immediately took to Twitter, self-organizing to help law enforcement agencies identify and charge the rioters. Their investigation was impromptu, not orchestrated, and open to anyone, not just experts. Participants didn’t need a badge or a security clearance—just an Internet connection….(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)”.

Why Business Schools Need to Teach Experimentation

Curated on Posted on by Stefaan Verhulst

Elizabeth R. Tenney, Elaine Costa, and Ruchi M. Watson at Harvard Business Review: “…The value of experiments in nonscientific organizations is quite high. Instead of calling in managers to solve every puzzle or dispute large and small (Should we make the background yellow or blue? Should we improve basic functionality or add new features? Are staff properly supported and incentivized to provide rapid responses?), teams can run experiments and measure outcomes of interest and, armed with new data, decide for themselves, or at least put forward a proposal grounded in relevant information. The data also provide tangible deliverables to show to stakeholders to demonstrate progress and accountability.

Experiments spur innovation. They can provide proof of concept and a degree of confidence in new ideas before taking bigger risks and scaling up. When done well, with data collected and interpreted objectively, experiments can also provide a corrective for faulty intuition, inaccurate assumptions, or overconfidence. The scientific method (which powers experiments) is the gold standard of tools to combat bias and answer questions objectively.

But as more and more companies are embracing a culture of experimentation, they face a major challenge: talent. Experiments are difficult to do well. Some challenges include special statistical knowledge, clear problem definition, and interpretation of the results. And it’s not enough to have the skillset. Experiments should ideally be done iteratively, building on prior knowledge and working toward deeper understanding of the question at hand. There are also the issues of managers’ preparedness to override their intuition when data disagree with it, and their ability to navigate hierarchy and bureaucracy to implement changes based on the experiments’ outcomes.

Some companies seem to be hiring small armies of PhDs to meet these competency challenges. (Amazon, for example, employs more than 100 PhD economists.) This isn’t surprising, given that PhDs receive years of training — and that the shrinking tenure-track market in academia has created a glut of PhDs. Other companies are developing employees in-house, training them in narrow, industry-specific methodologies. For example, General Mills recently hired for their innovator incubator group, called g-works, advertising for employees who are “using entrepreneurial skills and an experimental mindset” in what they called a “test and learn environment, with rapid experimentation to validate or invalidate assumptions.” Other companies — including Fidelity, LinkedIn, and Aetna — have hired consultants to conduct experiments, among them Irrational Labs, cofounded by Duke University’s Dan Ariely and the behavioral economist Kristen Berman….(More)”.

Scientific publishing’s new weapon for the next crisis: the rapid correction

Curated on Posted on by Stefaan Verhulst

Gideon Meyerowitz-Katz and James Heathers at STATNews: “If evidence of errors does emerge, the process for correcting or withdrawing a paper tends to be alarmingly long. Late last year, for example, David Cox, the IBM director of the MIT-IBM Watson AI Lab, discovered that his name was included as an author on two papers he had never written. After he wrote to the journals involved, it took almost three months for them to remove his name and the papers themselves. In cases of large-scale research fraud, correction times can be measured in years.

Imagine now that the issue with a manuscript is not a simple matter of retracting a fraudulent paper, but a more complex methodological or statistical problem that undercuts the study’s conclusions. In this context, requests for clarification — or retraction — can languish for years. The process can outlast both the tenure of the responsible editor, resetting the clock on the entire ordeal, or the journal itself can cease publication, leaving an erroneous article in the public domain without oversight, forever….

This situation must change, and change quickly. Any crisis that requires scientific information in a hurry will produce hurried science, and hurried science often includes miscalculated analyses, poor experimental design, inappropriate statistical models, impossible numbers, or even fraud. Having the agility to produce and publicize work like this without having the ability to correct it just as quickly is a curiously persistent oversight in the global scientific enterprise. If corrections occur only long after the research has already been used to treat people across the world, what use are they at all?

There are some small steps in the right direction. The open-source website PubPeer aggregates formal scientific criticism, and when shoddy research makes it into the literature, hordes of critics may leave comments and questions on the site within hours. Twitter, likewise, is often abuzz with spectacular scientific critiques almost as soon as studies go up online.

But these volunteer efforts are not enough. Even when errors are glaring and obvious, the median response from academic journals is to deal with them grudgingly or not at all. Academia in general takes a faintly disapproving tone of crowd-sourced error correction, ignoring the fact that it is often the only mechanism that exists to do this vital work.

Scientific publishing needs to stop treating error-checking as a slightly inconvenient side note and make it a core part of academic research. In a perfect world, entire departmental sections would be dedicated to making sure that published research is correct and reliable. But even a few positions would be a fine start. Young researchers could be given kudos not just for every citation in their Google scholar profile but also for every post-publication review they undertake….(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)”.