Why we must break the constraints of the industrial model of government

Curated on Posted on by Stefaan Verhulst

Max Beverton Palmer at the New Statesman: “…In practice, governments must shift from delivering what they always have to ensuring people’s needs are met in the best possible way. This should open up delivery to partners from both the private and charity sectors, where they can provide a better service that delivers better value to citizens, and much greater engagement with the public.

To manage this shift, leaders will need to resolve three key trade-offs.

First, states must be able to give up control to encourage innovation while protecting quality and in-house capacity. They must create new frameworks to assess where to encourage more open policymaking and delivery and where to double down on the competencies and infrastructure only they can provide. Technology can help here, creating new levers to protect the public interest by governing services’ access to government platforms and datasets akin to app store guidelines.

Second, states must reorganise around scale economies underpinned by technology while moving delivery closer to people’s lives. They should provide the foundations that allow new services to operate, while letting go of controlling the last mile of service delivery. A better way forward is a more collaborative approach that encourages communities, charities and companies to design more tailored services on top of public-controlled infrastructure, enabling people to choose those which best meet their needs.

Third, governments must be able to better listen, engage with and adapt to peoples’ views without descending into mob-rule. A core part of product and service design both in business and in the public sector is iterating delivery according to user needs, but the feedback loops in policymaking are comparably non-existent. New tools can help leaders understand the plurality of public opinions and address the growing disconnect between public institutions and those they represent.

MaxGetting from the status quo to this more open model will be challenging. But action in four priority areas should provide a starting point: infrastructure, organisation, competition and engagement….(More)”

The secret to building a smart city that’s antiracist

Curated on Posted on by Stefaan Verhulst

Article by Eliza McCullough: “….Instead of a smart city model that extracts from, surveils, and displaces poor people of color, we need a democratic model that allows community members to decide how technological infrastructure operates and to ensure the equitable distribution of benefits. Doing so will allow us to create cities defined by inclusion, shared ownership, and shared prosperity.

In 2016, Barcelona, for example, launched its Digital City Plan, which aims to empower residents with control of technology used in their communities. The document incorporates over 8,000 proposals from residents and includes plans for open source software, government ownership of all ICT infrastructure, and a pilot platform to help citizens maintain control over their personal data. As a result, the city now has free applications that allow residents to easily propose city development ideas, actively participate in city council meetings, and choose how their data is shared.

In the U.S., we need a framework for tech sovereignty that incorporates a racial equity approach: In a racist society, race neutrality facilitates continued exclusion and exploitation of people of color. Digital Justice Lab in Toronto illustrates one critical element of this kind of approach: access to information. In 2018, the organization gave community groups a series of grants to hold public events that shared resources and information about digital rights. Their collaborative approach intentionally focuses on the specific needs of people of color and other marginalized groups.

The turn toward intensified surveillance infrastructure in the midst of the coronavirus outbreak makes the need to adopt such practices all the more crucial. Democratic tech models that uplift marginalized populations provide us the chance to build a city that is just and open to everyone….(More)”.

Why Modeling the Spread of COVID-19 Is So Damn Hard


Curated on Posted on by Stefaan Verhulst

Matthew Hutson at IEEE Spectrum: “…Researchers say they’ve learned a lot of lessons modeling this pandemic, lessons that will carry over to the next.

The first set of lessons is all about data. Garbage in, garbage out, they say. Jarad Niemi, an associate professor of statistics at Iowa State University who helps run the forecast hub used by the CDC, says it’s not clear what we should be predicting. Infections, deaths, and hospitalization numbers each have problems, which affect their usefulness not only as inputs for the model but also as outputs. It’s hard to know the true number of infections when not everyone is tested. Deaths are easier to count, but they lag weeks behind infections. Hospitalization numbers have immense practical importance for planning, but not all hospitals release those figures. How useful is it to predict those numbers if you never have the true numbers for comparison? What we need, he said, is systematized random testing of the population, to provide clear statistics of both the number of people currently infected and the number of people who have antibodies against the virus, indicating recovery. Prakash, of Georgia Tech, says governments should collect and release data quickly in centralized locations. He also advocates for central repositories of policy decisions, so modelers can quickly see which areas are implementing which distancing measures.

Researchers also talked about the need for a diversity of models. At the most basic level, averaging an ensemble of forecasts improves reliability. More important, each type of model has its own uses—and pitfalls. An SEIR model is a relatively simple tool for making long-term forecasts, but the devil is in the details of its parameters: How do you set those to match real-world conditions now and into the future? Get them wrong and the model can head off into fantasyland. Data-driven models can make accurate short-term forecasts, and machine learning may be good for predicting complicated factors. But will the inscrutable computations of, for instance, a neural network remain reliable when conditions change? Agent-based models look ideal for simulating possible interventions to guide policy, but they’re a lot of work to build and tricky to calibrate.

Finally, researchers emphasize the need for agility. Niemi of Iowa State says software packages have made it easier to build models quickly, and the code-sharing site GitHub lets people share and compare their models. COVID-19 is giving modelers a chance to try out all their newest tools, says Meyers, of the University of Texas. “The pace of innovation, the pace of development, is unlike ever before,” she says. “There are new statistical methods, new kinds of data, new model structures.”…(More)”.

Public Sector Tech: New tools for the new normal

Curated on Posted on by Stefaan Verhulst

Special issue by ZDNet exploring “how new technologies like AI, cloud, drones, and 5G are helping government agencies, public organizations, and private companies respond to the events of today and tomorrow…:

The Cruel New Era of Data-Driven Deportation

Curated on Posted on by Stefaan Verhulst

Article by Alvaro M. Bedoya: “For a long time, mass deportations were a small-data affair, driven by tips, one-off investigations, or animus-driven hunches. But beginning under George W. Bush, and expanding under Barack Obama, ICE leadership started to reap the benefits of Big Data. The centerpiece of that shift was the “Secure Communities” program, which gathered the fingerprints of arrestees at local and state jails across the nation and compared them with immigration records. That program quickly became a major driver for interior deportations. But ICE wanted more data. The agency had long tapped into driver address records through law enforcement networks. Eyeing the breadth of DMV databases, agents began to ask state officials to run face recognition searches on driver photos against the photos of undocumented people. In Utah, for example, ICE officers requested hundreds of face searches starting in late 2015. Many immigrants avoid contact with any government agency, even the DMV, but they can’t go without heat, electricity, or water; ICE aimed to find them, too. So, that same year, ICE paid for access to a private database that includes the addresses of customers from 80 national and regional electric, cable, gas, and telephone companies.

Amid this bonanza, at least, the Obama administration still acknowledged red lines. Some data were too invasive, some uses too immoral. Under Donald Trump, these limits fell away.

In 2017, breaking with prior practice, ICE started to use data from interviews with scared, detained kids and their relatives to find and arrest more than 500 sponsors who stepped forward to take in the children. At the same time, ICE announced a plan for a social media monitoring program that would use artificial intelligence to automatically flag 10,000 people per month for deportation investigations. (It was scuttled only when computer scientists helpfully indicated that the proposed system was impossible.) The next year, ICE secured access to 5 billion license plate scans from public parking lots and roadways, a hoard that tracks the drives of 60 percent of Americans—an initiative blocked by Department of Homeland Security leadership four years earlier. In August, the agency cut a deal with Clearview AI, whose technology identifies people by comparing their faces not to millions of driver photos, but to 3 billion images from social media and other sites. This is a new era of immigrant surveillance: ICE has transformed from an agency that tracks some people sometimes to an agency that can track anyone at any time….(More)”.

AI planners in Minecraft could help machines design better cities

Curated on Posted on by Stefaan Verhulst

Article by Will Douglas Heaven: “A dozen or so steep-roofed buildings cling to the edges of an open-pit mine. High above them, on top of an enormous rock arch, sits an inaccessible house. Elsewhere, a railway on stilts circles a group of multicolored tower blocks. Ornate pagodas decorate a large paved plaza. And a lone windmill turns on an island, surrounded by square pigs. This is Minecraft city-building, AI style.

Minecraft has long been a canvas for wild invention. Fans have used the hit block-building game to create replicas of everything from downtown Chicago and King’s Landing to working CPUs. In the decade since its first release, anything that can be built has been.

Since 2018, Minecraft has also been the setting for a creative challenge that stretches the abilities of machines. The annual Generative Design in Minecraft (GDMC) competition asks participants to build an artificial intelligence that can generate realistic towns or villages in previously unseen locations. The contest is just for fun, for now, but the techniques explored by the various AI competitors are precursors of ones that real-world city planners could use….(More)”.

Global citizen deliberation on genome editing

Curated on Posted on by Stefaan Verhulst

Essay by John S. Dryzek et al at Science: “Genome editing technologies provide vast possibilities for societal benefit, but also substantial risks and ethical challenges. Governance and regulation of such technologies have not kept pace in a systematic or internationally consistent manner, leaving a complex, uneven, and incomplete web of national and international regulation (1). How countries choose to regulate these emergent technologies matters not just locally, but globally, because the implications of technological developments do not stop at national boundaries. Practices deemed unacceptable in one country may find a more permissive home in another: not necessarily through national policy choice, but owing to a persistent national legal and regulatory void that enables “ethics dumping” (2)—for example, if those wanting to edit genes to “perfect” humans seek countries with little governance capacity. Just as human rights are generally recognized as a matter of global concern, so too should technologies that may impinge on the question of what it means to be human. Here we show how, as the global governance vacuum is filled, deliberation by a global citizens’ assembly should play a role, for legitimate and effective governance….(More)”.

Politicians should take citizens’ assemblies seriously

Curated on Posted on by Stefaan Verhulst

The Economist: “In 403bc Athens decided to overhaul its institutions. A disastrous war with Sparta had shown that direct democracy, whereby adult male citizens voted on laws, was not enough to stop eloquent demagogues from getting what they wanted, and indeed from subverting democracy altogether. So a new body, chosen by lot, was set up to scrutinise the decisions of voters. It was called the nomothetai or “layers down of law” and it would be given the time to ponder difficult decisions, unmolested by silver-tongued orators and the schemes of ambitious politicians.

This ancient idea is back in vogue, and not before time. Around the world “citizens’ assemblies” and other deliberative groups are being created to consider questions that politicians have struggled to answer (see article). Over weeks or months, 100 or so citizens—picked at random, but with a view to creating a body reflective of the population as a whole in terms of gender, age, income and education—meet to discuss a divisive topic in a considered, careful way. Often they are paid for their time, to ensure that it is not just political wonks who sign up. At the end they present their recommendations to politicians. Before covid-19 these citizens met in conference centres in large cities where, by mingling over lunch-breaks, they discovered that the monsters who disagree with them turned out to be human after all. Now, as a result of the pandemic, they mostly gather on Zoom.

Citizens’ assemblies are often promoted as a way to reverse the decline in trust in democracy, which has been precipitous in most of the developed world over the past decade or so. Last year the majority of people polled in America, Britain, France and Australia—along with many other rich countries—felt that, regardless of which party wins an election, nothing really changes. Politicians, a common complaint runs, have no understanding of, or interest in, the lives and concerns of ordinary people.

Citizens’ assemblies can help remedy that. They are not a substitute for the everyday business of legislating, but a way to break the deadlock when politicians have tried to deal with important issues and failed. Ordinary people, it turns out, are quite reasonable. A large four-day deliberative experiment in America softened Republicans’ views on immigration; Democrats became less eager to raise the minimum wage. Even more strikingly, two 18-month-long citizens’ assemblies in Ireland showed that the country, despite its deep Catholic roots, was far more socially liberal than politicians had realised. Assemblies overwhelmingly recommended the legalisation of both same-sex marriage and abortion….(More)”.

How Tech Companies Can Advance Data Science for Social Good

Curated on Posted on by Stefaan Verhulst

Essay by Nick Martin: “As the world struggles to achieve the UN’s Sustainable Development Goals (SDGs), the need for reliable data to track our progress is more important than ever. Government, civil society, and private sector organizations all play a role in producing, sharing, and using this data, but their information-gathering and -analysis efforts have been able to shed light on only 68 percent of the SDG indicators so far, according to a 2019 UN study.

To help fill the gap, the data science for social good (DSSG) movement has for years been making datasets about important social issues—such as health care infrastructure, school enrollment, air quality, and business registrations—available to trusted organizations or the public. Large tech companies such as Facebook, Google, Amazon, and others have recently begun to embrace the DSSG movement. Spurred on by advances in the field, the Development Data Partnership, the World Economic Forum’s 2030Vision consortium, and Data Collaboratives, they’re offering information about social media users’ mobility during COVID-19, cloud computing infrastructure to help nonprofits analyze large datasets, and other important tools and services.

But sharing data resources doesn’t mean they’ll be used effectively, if at all, to advance social impact. High-impact results require recipients of data assistance to inhabit a robust, holistic data ecosystem that includes assets like policies for safely handling data and the skills to analyze it. As tech firms become increasingly involved with using data and data science to help achieve the SDGs, it’s important that they understand the possibilities and limitations of the nonprofits and other civil society organizations they’re working with. Without a firm grasp on the data ecosystems of their partners, all the technical wizardry in the world may be for naught.

Companies must ask questions such as: What incentives or disincentives are in place for nonprofits to experiment with data science in their work? What gaps remain between what nonprofits or data scientists need and the resources funders provide? What skills must be developed? To help find answers, TechChange, an organization dedicated to using technology for social good, partnered with Project17, Facebook’s partnerships-led initiative to accelerate progress on the SDGs. Over the past six months, the team led interviews with top figures in the DSSG community from industry, academia, and the public sector. The 14 experts shared numerous insights into using data and data science to advance social good and the SDGs. Four takeaways emerged from our conversations and research…(More)”.

Ethical Challenges and Opportunities Associated With the Ability to Perform Medical Screening From Interactions With Search Engines

Curated on Posted on by Stefaan Verhulst

Viewpoint by Elad Yom-Tov and Yuval Cherlow: “Recent research has shown the efficacy of screening for serious medical conditions from data collected while people interact with online services. In particular, queries to search engines and the interactions with them were shown to be advantageous for screening a range of conditions including diabetes, several forms of cancer, eating disorders, and depression. These screening abilities offer unique advantages in that they can serve a broad strata of the society, including people in underserved populations and in countries with poor access to medical services. However, these advantages need to be balanced against the potential harm to privacy, autonomy, and nonmaleficence, which are recognized as the cornerstones of ethical medical care. Here, we discuss these opportunities and challenges, both when collecting data to develop online screening services and when deploying them. We offer several solutions that balance the advantages of these services with the ethical challenges they pose….(More)”.