Once upon a bureaucrat: Exploring the role of stories in government

Curated on Posted on by Stefaan Verhulst

Article by Thea Snow: “When you think of a profession associated with stories, what comes to mind? Journalist, perhaps? Or author? Maybe, at a stretch, you might think about a filmmaker. But I would hazard a guess that “public servant” would unlikely be one of the first professions that come to mind. However, recent research suggests that we should be thinking more deeply about the connections between stories and government.

Since 2021, the Centre for Public Impact, in partnership with Dusseldorp Forum and Hands Up Mallee, has been exploring the role of storytelling in the context of place-based systems change work. Our first report, Storytelling for Systems Change: Insights from the Field, focused on the way communities use stories to support place-based change. Our second report, Storytelling for Systems Change: Listening to Understand, focused more on how stories are perceived and used by those in government who are funding and supporting community-led systems change initiatives.

To shape these reports, we have spent the past few years speaking to community members, collective impact backbone teams, storytelling experts, academics, public servants, data analysts, and more. Here’s some of what we’ve heard…(More)”.

Understanding and Measuring Hype Around Emergent Technologies

Curated on Posted on by Stefaan Verhulst

Article by Swaptik Chowdhury and Timothy Marler: “Inaccurate or excessive hype surrounding emerging technologies can have several negative effects, including poor decisionmaking by both private companies and the U.S. government. The United States needs a comprehensive approach to understanding and assessing public discourse–driven hype surrounding emerging technologies, but current methods for measuring technology hype are insufficient for developing policies to manage it. The authors of this paper describe an approach to analyzing technology hype…(More)”.

Evidence for policy-makers: A matter of timing and certainty?

Curated on Posted on by Stefaan Verhulst

Article by Wouter Lammers et al: “This article investigates how certainty and timing of evidence introduction impact the uptake of evidence by policy-makers in collective deliberations. Little is known about how experts or researchers should time the introduction of uncertain evidence for policy-makers. With a computational model based on the Hegselmann–Krause opinion dynamics model, we simulate how policy-makers update their opinions in light of new evidence. We illustrate the use of our model with two examples in which timing and certainty matter for policy-making: intelligence analysts scouting potential terrorist activity and food safety inspections of chicken meat. Our computations indicate that evidence should come early to convince policy-makers, regardless of how certain it is. Even if the evidence is quite certain, it will not convince all policy-makers. Next to its substantive contribution, the article also showcases the methodological innovation that agent-based models can bring for a better understanding of the science–policy nexus. The model can be endlessly adapted to generate hypotheses and simulate interactions that cannot be empirically tested…(More)”.

A World Divided Over Artificial Intelligence

Curated on Posted on by Stefaan Verhulst

Article by Aziz Huq: “…Through multinational communiqués and bilateral talks, an international framework for regulating AI does seem to be coalescing. Take a close look at U.S. President Joe Biden’s October 2023 executive order on AI; the EU’s AI Act, which passed the European Parliament in December 2023 and will likely be finalized later this year; or China’s slate of recent regulations on the topic, and a surprising degree of convergence appears. They have much in common. These regimes broadly share the common goal of preventing AI’s misuse without restraining innovation in the process. Optimists have floated proposals for closer international management of AI, such as the ideas presented in Foreign Affairs by the geopolitical analyst Ian Bremmer and the entrepreneur Mustafa Suleyman and the plan offered by Suleyman and Eric Schmidt, the former CEO of Google, in the Financial Times in which they called for the creation of an international panel akin to the UN’s Intergovernmental Panel on Climate Change to “inform governments about the current state of AI capabilities and make evidence-based predictions about what’s coming.”

But these ambitious plans to forge a new global governance regime for AI may collide with an unfortunate obstacle: cold reality. The great powers, namely, China, the United States, and the EU, may insist publicly that they want to cooperate on regulating AI, but their actions point toward a future of fragmentation and competition. Divergent legal regimes are emerging that will frustrate any cooperation when it comes to access to semiconductors, the setting of technical standards, and the regulation of data and algorithms. This path doesn’t lead to a coherent, contiguous global space for uniform AI-related rules but to a divided landscape of warring regulatory blocs—a world in which the lofty idea that AI can be harnessed for the common good is dashed on the rocks of geopolitical tensions…(More)”.

The Limits of Data

Curated on Posted on by Stefaan Verhulst

Essay by C.Thi Nguyen: “…Right now, the language of policymaking is data. (I’m talking about “data” here as a concept, not as particular measurements.) Government agencies, corporations, and other policymakers all want to make decisions based on clear data about positive outcomes.  They want to succeed on the metrics—to succeed in clear, objective, and publicly comprehensible terms. But metrics and data are incomplete by their basic nature. Every data collection method is constrained and every dataset is filtered.

Some very important things don’t make their way into the data. It’s easier to justify health care decisions in terms of measurable outcomes: increased average longevity or increased numbers of lives saved in emergency room visits, for example. But there are so many important factors that are far harder to measure: happiness, community, tradition, beauty, comfort, and all the oddities that go into “quality of life.”

Consider, for example, a policy proposal that doctors should urge patients to sharply lower their saturated fat intake. This should lead to better health outcomes, at least for those that are easier to measure: heart attack numbers and average longevity. But the focus on easy-to-measure outcomes often diminishes the salience of other downstream consequences: the loss of culinary traditions, disconnection from a culinary heritage, and a reduction in daily culinary joy. It’s easy to dismiss such things as “intangibles.” But actually, what’s more tangible than a good cheese, or a cheerful fondue party with friends?…(More)”.

Automakers Are Sharing Consumers’ Driving Behavior With Insurance Companies

Curated on Posted on by Stefaan Verhulst

Article by Kashmir Hill: “Kenn Dahl says he has always been a careful driver. The owner of a software company near Seattle, he drives a leased Chevrolet Bolt. He’s never been responsible for an accident.

So Mr. Dahl, 65, was surprised in 2022 when the cost of his car insurance jumped by 21 percent. Quotes from other insurance companies were also high. One insurance agent told him his LexisNexis report was a factor.

LexisNexis is a New York-based global data broker with a “Risk Solutions” division that caters to the auto insurance industry and has traditionally kept tabs on car accidents and tickets. Upon Mr. Dahl’s request, LexisNexis sent him a 258-page “consumer disclosure report,” which it must provide per the Fair Credit Reporting Act.

What it contained stunned him: more than 130 pages detailing each time he or his wife had driven the Bolt over the previous six months. It included the dates of 640 trips, their start and end times, the distance driven and an accounting of any speeding, hard braking or sharp accelerations. The only thing it didn’t have is where they had driven the car.

On a Thursday morning in June for example, the car had been driven 7.33 miles in 18 minutes; there had been two rapid accelerations and two incidents of hard braking.

According to the report, the trip details had been provided by General Motors — the manufacturer of the Chevy Bolt. LexisNexis analyzed that driving data to create a risk score “for insurers to use as one factor of many to create more personalized insurance coverage,” according to a LexisNexis spokesman, Dean Carney. Eight insurance companies had requested information about Mr. Dahl from LexisNexis over the previous month.

“It felt like a betrayal,” Mr. Dahl said. “They’re taking information that I didn’t realize was going to be shared and screwing with our insurance.”..(More)”.

A Plan to Develop Open Science’s Green Shoots into a Thriving Garden

Curated on Posted on by Stefaan Verhulst

Article by Greg Tananbaum, Chelle Gentemann, Kamran Naim, and Christopher Steven Marcum: “…As it’s moved from an abstract set of principles about access to research and data into the realm of real-world activities, the open science movement has mirrored some of the characteristics of the open source movement: distributed, independent, with loosely coordinated actions happening in different places at different levels. Globally, many things are happening, often disconnected, but still interrelated: open science has sowed a constellation of thriving green shoots, not quite yet a garden, but all growing rapidly on arable soil.

Streamlining research processes, reducing duplication of efforts, and accelerating scientific discoveries could ensure that the fruits of open science processes and products are more accessible and equitably distributed.

It is now time to consider how much faster and farther the open science movement could go with more coordination. What efficiencies might be realized if disparate efforts could better harmonize across geographies, disciplines, and sectors? How would an intentional, systems-level approach to aligning incentives, infrastructure, training, and other key components of a rationally functioning research ecosystem advance the wider goals of the movement? Streamlining research processes, reducing duplication of efforts, and accelerating scientific discoveries could ensure that the fruits of open science processes and products are more accessible and equitably distributed…(More)”

UNESCO’s Quest to Save the World’s Intangible Heritage

Curated on Posted on by Stefaan Verhulst

Article by Julian Lucas: “For decades, the organization has maintained a system that protects everything from Ukrainian borscht to Jamaican reggae. But what does it mean to “safeguard” living culture?…On December 7th, at a safari resort in Kasane, Botswana, Ukraine briefed the United Nations Educational, Scientific, and Cultural Organization (UNESCO) on an endangered national treasure. It wasn’t a monastery menaced by air strikes. Nor was it any of the paintings, rare books, or other antiquities seized by Russian troops. It was borscht, a beet soup popular for centuries across Eastern Europe. Shortly after Russia invaded Ukraine, in February, 2022—as fields burned, restaurants shuttered, and expert cooks fled their homes—Kyiv successfully petitioned UNESCO to add its culture of borscht-making to the List of Intangible Cultural Heritage in Need of Urgent Safeguarding. Now, despite setbacks on the battlefield, the state of the soup was strong. A Ukrainian official reported on her government’s new borscht-related initiatives, such as hosting gastronomic festivals and inventorying vulnerable recipes. She looked forward to borscht’s graduation from Urgent Safeguarding to the Representative List of the Intangible Cultural Heritage (I.C.H.) of Humanity—which grew, that session, to include Italian opera singing, Bangladeshi rickshaw painting, Angolan sand art, and Peruvian ceviche…(More)”.

Synthetic Data and the Future of AI

Curated on Posted on by Stefaan Verhulst

Paper by Peter Lee: “The future of artificial intelligence (AI) is synthetic. Several of the most prominent technical and legal challenges of AI derive from the need to amass huge amounts of real-world data to train machine learning (ML) models. Collecting such real-world data can be highly difficult and can threaten privacy, introduce bias in automated decision making, and infringe copyrights on a massive scale. This Article explores the emergence of a seemingly paradoxical technical creation that can mitigate—though not completely eliminate—these concerns: synthetic data. Increasingly, data scientists are using simulated driving environments, fabricated medical records, fake images, and other forms of synthetic data to train ML models. Artificial data, in other words, is being used to train artificial intelligence. Synthetic data offers a host of technical and legal benefits; it promises to radically decrease the cost of obtaining data, sidestep privacy issues, reduce automated discrimination, and avoid copyright infringement. Alongside such promise, however, synthetic data offers perils as well. Deficiencies in the development and deployment of synthetic data can exacerbate the dangers of AI and cause significant social harm.

In light of the enormous value and importance of synthetic data, this Article sketches the contours of an innovation ecosystem to promote its robust and responsible development. It identifies three objectives that should guide legal and policy measures shaping the creation of synthetic data: provisioning, disclosure, and democratization. Ideally, such an ecosystem should incentivize the generation of high-quality synthetic data, encourage disclosure of both synthetic data and processes for generating it, and promote multiple sources of innovation. This Article then examines a suite of “innovation mechanisms” that can advance these objectives, ranging from open source production to proprietary approaches based on patents, trade secrets, and copyrights. Throughout, it suggests policy and doctrinal reforms to enhance innovation, transparency, and democratic access to synthetic data. Just as AI will have enormous legal implications, law and policy can play a central role in shaping the future of AI…(More)”.

The Future of Trust

Curated on Posted on by Stefaan Verhulst

Book by Ros Taylor: “In a society battered by economic, political, cultural and ecological collapse, where do we place our trust, now that it is more vital than ever for our survival? How has that trust – in our laws, our media, our governments – been lost, and how can it be won back? Examining the police, the rule of law, artificial intelligence, the 21st century city and social media, Ros Taylor imagines what life might be like in years to come if trust continues to erode.

Have conspiracy theories permanently damaged our society? Will technological advances, which require more and more of our human selves, ultimately be rejected by future generations? And in a world fast approaching irreversible levels of ecological damage, how can we trust the custodians of these institutions to do the right thing – even as humanity faces catastrophe?…(More)”.