New Orleans is using sentiment analysis on federal relief funding

Curated on Posted on by Stefaan Verhulst

Ryan Johnston at StateScoop: “New Orleans is using data and social-media analysis to gauge how residents want the city to spend $375 million in federal stimulus funding, while quelling concerns of corruption or misuse that still exist from the city’s Hurricane Katrina recovery, officials told StateScoop on Tuesday.

The city government is working with ZenCity, an Israeli data-analysis firm that trawls social media to better understand how residents feel about various issues, to research American Rescue Plan funding. New Orleans is set to receive $375 million in relief funding to stabilize its finances and, “directly address” the economic impact that the COVID-19 pandemic had on the city, said Liana Elliot, the city’s deputy chief of staff. But many residents of the city are still wary of how the city squandered its Federal Emergency Management Agency funding following the natural disaster in 2005.

That caution became apparent almost immediately in online discourse, said Eyal Feder-Levy, ZenCity’s chief executive.

“We saw within the data that conversations about city budgets online in New Orleans were five-times more frequent than normal following the ARPA stimulus funding announcement,” Feder-Levy told StateScoop.

Elliot said what she heard about the budget in public didn’t match the conversations she was having with her colleagues in city government. Residents, she said, had an expectation that the money would help them, rather than go to city agencies…(More)”.

The tyranny of spreadsheets

Curated on Posted on by Stefaan Verhulst

Tim Harford at the Financial Times: “Early last October my phone rang. On the line was a researcher calling from Today, the BBC’s agenda-setting morning radio programme. She told me that something strange had happened, and she hoped I might be able to explain it. Nearly 16,000 positive Covid cases had disappeared completely from the UK’s contact tracing system. These were 16,000 people who should have been warned they were infected and a danger to others, 16,000 cases contact tracers should have been running down to figure out where the infected went, who they met and who else might be at risk. None of which was happening. Why had the cases disappeared? Apparently, Microsoft Excel had run out of numbers.

It was an astonishing story that would, in time, lead me to delve into the history of accountancy, epidemiology and vaccination, discuss file formatting with Microsoft’s founder, Bill Gates, and even trace the aftershocks of the collapse of Enron. But above all, it was a story that would teach me about the way we take numbers for granted….

The origin of Excel can be traced back far further than that of Microsoft. In the late 1300s, the need for a solid system for accounts was evident in the outbursts of one man in particular, an Italian textile merchant named Francesco di Marco Datini. Poor Datini was surrounded by fools.

“You cannot see a crow in a bowlful of milk!” he berated one associate.

“You could lose your way from your nose to your mouth!” he chided another.

Iris Origo’s vivid book The Merchant of Prato describes Datini’s everyday life and explains his problem: keeping track of everything in a complicated world. By the end of the 14th century, merchants such as Datini had progressed from mere travelling salesmen able to keep track of profits by patting their purses. They were now in charge of sophisticated operations.

Datini, for example, ordered wool from the island of Mallorca two years before the sheep had even grown it, a hedge to account for the numerous subcontractors that would process it before it became beautiful rolls of dyed cloth. The supply chain between shepherd and consumer stretched across Barcelona, Pisa, Venice, Valencia, North Africa and back to Mallorca. It took four years between the initial order of wool and the final sale of cloth.

No wonder Datini insisted on absolute clarity about where his product was at any moment, not to mention his money. How did he manage? Spreadsheets…(More)”

Seek diversity to solve complexity

Curated on Posted on by Stefaan Verhulst

Katrin Prager at Nature: “As a social scientist, I know that one person cannot solve a societal problem on their own — and even a group of very intelligent people will struggle to do it. But we can boost our chances of success if we ensure not only that the team members are intelligent, but also that the team itself is highly diverse.

By ‘diverse’ I mean demographic diversity encompassing things such as race, gender identity, class, ethnicity, career stage and age, and cognitive diversity, including differences in thoughts, insights, disciplines, perspectives, frames of reference and thinking styles. And the team needs to be purposely diverse instead of arbitrarily diverse.

In my work I focus on complex world problems, such as how to sustainably manage our natural resources and landscapes, and I’ve found that it helps to deliberately assemble diverse teams. This effort requires me to be aware of the different ways in which people can be diverse, and to reflect on my own preferences and biases. Sometimes the teams might not be as diverse as I’d like. But I’ve found that making the effort not only to encourage diversity, but also to foster better understanding between team members reaps dividends….(more)”

How to be a good ancestor

Curated on Posted on by Stefaan Verhulst

Article by Sigal Samuel: “In 2015, 20 residents of Yahaba, a small town in northeastern Japan, went to their town hall to take part in a unique experiment.

Their goal was to design policies that would shape the future of Yahaba. They would debate questions typically reserved for politicians: Would it be better to invest in infrastructure or child care? Should we promote renewable energy or industrial farming?

But there was a twist. While half the citizens were invited to be themselves and express their own opinions, the remaining participants were asked to put on special ceremonial robes and play the part of people from the future. Specifically, they were told to imagine they were from the year 2060, meaning they’d be representing the interests of a future generation during group deliberations.

What unfolded was striking. The citizens who were just being themselves advocated for policies that would boost their lifestyle in the short term. But the people in robes advocated for much more radical policies — from massive health care investments to climate change action — that would be better for the town in the long term. They managed to convince their fellow citizens that taking that approach would benefit their grandkids. In the end, the entire group reached a consensus that they should, in some ways, act against their own immediate self-interest in order to help the future.

This experiment marked the beginning of Japan’s Future Design movement. What started in Yahaba has since been replicated in city halls around the country, feeding directly into real policymaking. It’s one example of a burgeoning global attempt to answer big moral questions: Do we owe it to future generations to take their interests into account? What does it look like to incorporate the preferences of people who don’t even exist yet? How can we be good ancestors?…(More)”.

The Social Sector Needs a Meta Movement

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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

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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)”.