How cities are reinventing the public-private partnership − 4 lessons from around the globe

Curated on Posted on by Stefaan Verhulst

Article by Debra Lam: “Cities tackle a vast array of responsibilities – from building transit networks to running schools – and sometimes they can use a little help. That’s why local governments have long teamed up with businesses in so-called public-private partnerships. Historically, these arrangements have helped cities fund big infrastructure projects such as bridges and hospitals.

However, our analysis and research show an emerging trend with local governments engaged in private-sector collaborations – what we have come to describe as “community-centered, public-private partnerships,” or CP3s. Unlike traditional public-private partnerships, CP3s aren’t just about financial investments; they leverage relationships and trust. And they’re about more than just building infrastructure; they’re about building resilient and inclusive communities.

As the founding executive director of the Partnership for Inclusive Innovation, based out of the Georgia Institute of Technology, I’m fascinated with CP3s. And while not all CP3s are successful, when done right they offer local governments a powerful tool to navigate the complexities of modern urban life.

Together with international climate finance expert Andrea Fernández of the urban climate leadership group C40, we analyzed community-centered, public-private partnerships across the world and put together eight case studies. Together, they offer valuable insights into how cities can harness the power of CP3s.

4 keys to success

Although we looked at partnerships forged in different countries and contexts, we saw several elements emerge as critical to success over and over again.

• 1. Clear mission and vision: It’s essential to have a mission that resonates with everyone involved. Ruta N in Medellín, Colombia, for example, transformed the city into a hub of innovation, attracting 471 technology companies and creating 22,500 jobs.

This vision wasn’t static. It evolved in response to changing local dynamics, including leadership priorities and broader global trends. However, the core mission of entrepreneurship, investment and innovation remained clear and was embraced by all key stakeholders, driving the partnership forward.

 2. Diverse and engaged partners: Successful CP3s rely on the active involvement of a wide range of partners, each bringing their unique expertise and resources to the table. In the U.K., for example, the Hull net-zero climate initiative featured a partnership that included more than 150 companies, many small and medium-size. This diversity of partners was crucial to the initiative’s success because they could leverage resources and share risks, enabling it to address complex challenges from multiple angles.

Similarly, Malaysia’s Think City engaged community-based organizations and vulnerable populations in its Penang climate adaptation program. This ensured that the partnership was inclusive and responsive to the needs of all citizens…(More)”.

How many yottabytes in a quettabyte? Extreme numbers get new names

Curated on Posted on by Stefaan Verhulst

Article by Elizabeth Gibney: “By the 2030s, the world will generate around a yottabyte of data per year — that’s 1024 bytes, or the amount that would fit on DVDs stacked all the way to Mars. Now, the booming growth of the data sphere has prompted the governors of the metric system to agree on new prefixes beyond that magnitude, to describe the outrageously big and small.

Representatives from governments worldwide, meeting at the General Conference on Weights and Measures (CGPM) outside Paris on 18 November, voted to introduce four new prefixes to the International System of Units (SI) with immediate effect. The prefixes ronna and quetta represent 1027 and 1030, and ronto and quecto signify 10−27 and 10−30. Earth’s mass is on the order of a ronnagram, and an electron’s mass is about one rontogram.

This is the first update to the prefix system since 1991, when the organization added zetta (1021), zepto (10−21), yotta (1024) and yocto (10−24). In that case, metrologists were adapting to fit the needs of chemists, who wanted a way to express SI units on the scale of Avogadro’s number — the 6 × 1023 units in a mole, a measure of the quantity of substances. The more familiar prefixes peta and exa were added in 1975 (see ‘Extreme figures’)…(More)”.

The AI revolution is running out of data. What can researchers do?

Curated on Posted on by Stefaan Verhulst

Article by Nicola Jones: “The Internet is a vast ocean of human knowledge, but it isn’t infinite. And artificial intelligence (AI) researchers have nearly sucked it dry.

The past decade of explosive improvement in AI has been driven in large part by making neural networks bigger and training them on ever-more data. This scaling has proved surprisingly effective at making large language models (LLMs) — such as those that power the chatbot ChatGPT — both more capable of replicating conversational language and of developing emergent properties such as reasoning. But some specialists say that we are now approaching the limits of scaling. That’s in part because of the ballooning energy requirements for computing. But it’s also because LLM developers are running out of the conventional data sets used to train their models.

A prominent study1 made headlines this year by putting a number on this problem: researchers at Epoch AI, a virtual research institute, projected that, by around 2028, the typical size of data set used to train an AI model will reach the same size as the total estimated stock of public online text. In other words, AI is likely to run out of training data in about four years’ time (see ‘Running out of data’). At the same time, data owners — such as newspaper publishers — are starting to crack down on how their content can be used, tightening access even more. That’s causing a crisis in the size of the ‘data commons’, says Shayne Longpre, an AI researcher at the Massachusetts Institute of Technology in Cambridge who leads the Data Provenance Initiative, a grass-roots organization that conducts audits of AI data sets.

The imminent bottleneck in training data could be starting to pinch. “I strongly suspect that’s already happening,” says Longpre…(More)”

Running out of data: Chart showing projections of the amount of text data used to train large language models and the amount of available text on the Internet, suggesting that by 2028, developers will be using data sets that match the total amount of text that is available.

Can the world’s most successful index get back up the rankings?

Curated on Posted on by Stefaan Verhulst

Article by James Watson: “You know your ranking model is influential when national governments change policies with the explicit goal of boosting their position on your index. That was the power of the Ease of Doing Business Index (also known as Doing Business) until 2021.

However, the index’s success became its downfall. Some governments set up dedicated teams with an explicit goal of improving the country’s performance on the index. If those teams’ activity was solely focussed on positive policy reform, that would be great; unfortunately, in at least some cases, they were simply trying to game the results.

World Bank’s Business Ready Index

Index ranking optimisation (aka gaming the results)

To give an example of how that could happen, we need to take a brief detour into the world of qualitative indicators. Bear with me. In many indexes grappling with complex topics, there is a perennial problem of data availability. Imagine you want to measure the number of days it takes to set up a new business (this was one of the indicators in Doing Business). You will find that most of the time the data either doesn’t exist or is rarely updated by governments. Instead, put very simplistically, you’d need to ask a few experts or businesses for their views, and use those to create a numerical score for your index.

This is a valid approach, and it’s used in a lot of studies. Take Transparency International’s long-running Corruption Perceptions Index (CPI). Transparency International goes to great lengths to use robust and comparable data across countries, but measuring actual corruption is not viable — for obvious reasons. So the CPI does something different, and the clue is in the name: it measures people’s perceptions of corruption. It asks local businesses and experts whether they think there’s much bribery, nepotism and other forms of corruption in their country. This foundational input is then bolstered with other data points. The data doesn’t aim to measure corruption; instead, it’s about assessing which countries are more, or less, corrupt. 

Transparency International’s Corruption Perceptions Index (CPI)

This technique can work well, but it got a bit shaky as Doing Business’s fame grew. Some governments that were anxious to move up the rankings started urging the World Bank to tweak the methodology used to assess their ratings, or to use the views of specific experts. The analysts responsible for assessing a country’s scores and data points were put under significant pressure, often facing strong criticism from governments that didn’t agree with their assessments. In the end, an internal review showed that a number of countries’ scores had been improperly manipulated…The criticism must have stung, because the team behind the World Bank’s new Business Ready report has spent three years trying to address those issues. The new methodology handbook lands with a thump at 704 pages…(More)”.

AI could help scale humanitarian responses. But it could also have big downsides

Curated on Posted on by Stefaan Verhulst

Article by Thalia Beaty: “As the International Rescue Committee copes with dramatic increases in displaced people in recent years, the refugee aid organization has looked for efficiencies wherever it can — including using artificial intelligence.

Since 2015, the IRC has invested in Signpost — a portfolio of mobile apps and social media channels that answer questions in different languages for people in dangerous situations. The Signpost project, which includes many other organizations, has reached 18 million people so far, but IRC wants to significantly increase its reach by using AI tools — if they can do so safely.

Conflict, climate emergencies and economic hardship have driven up demand for humanitarian assistance, with more than 117 million people forcibly displaced in 2024, according to the United Nations refugee agency. The turn to artificial intelligence technologies is in part driven by the massive gap between needs and resources.

To meet its goal of reaching half of displaced people within three years, the IRC is testing a network of AI chatbots to see if they can increase the capacity of their humanitarian officers and the local organizations that directly serve people through Signpost. For now, the pilot project operates in El Salvador, Kenya, Greece and Italy and responds in 11 languages. It draws on a combination of large language models from some of the biggest technology companies, including OpenAI, Anthropic and Google.

The chatbot response system also uses customer service software from Zendesk and receives other support from Google and Cisco Systems.

If they decide the tools work, the IRC wants to extend the technical infrastructure to other nonprofit humanitarian organizations at no cost. They hope to create shared technology resources that less technically focused organizations could use without having to negotiate directly with tech companies or manage the risks of deployment…(More)”.

Setting the Standard: Statistical Agencies’ Unique Role in Building Trustworthy AI

Curated on Posted on by Stefaan Verhulst

Article by Corinna Turbes: “As our national statistical agencies grapple with new challenges posed by artificial intelligence (AI), many agencies face intense pressure to embrace generative AI as a way to reach new audiences and demonstrate technological relevance. However, the rush to implement generative AI applications risks undermining these agencies’ fundamental role as authoritative data sources. Statistical agencies’ foundational mission—producing and disseminating high-quality, authoritative statistical information—requires a more measured approach to AI adoption.

Statistical agencies occupy a unique and vital position in our data ecosystem, entrusted with creating the reliable statistics that form the backbone of policy decisions, economic planning, and social research. The work of these agencies demands exceptional precision, transparency, and methodological rigor. Implementation of generative AI interfaces, while technologically impressive, could inadvertently compromise the very trust and accuracy that make these agencies indispensable.

While public-facing interfaces play a valuable role in democratizing access to statistical information, statistical agencies need not—and often should not—rely on generative AI to be effective in that effort. For statistical agencies, an extractive AI approach – which retrieves and presents existing information from verified databases rather than generating new content – offers a more appropriate path forward. By pulling from verified, structured datasets and providing precise, accurate responses, extractive AI systems can maintain the high standards of accuracy required while making statistical information more accessible to users who may find traditional databases overwhelming. An extractive, rather than generative,  approach allows agencies to modernize data delivery while preserving their core mission of providing reliable, verifiable statistical information…(More)”

Revealed: bias found in AI system used to detect UK benefits fraud

Curated on Posted on by Stefaan Verhulst

Article by Robert Booth: “An artificial intelligence system used by the UK government to detect welfare fraud is showing bias according to people’s age, disability, marital status and nationality, the Guardian can reveal.

An internal assessment of a machine-learning programme used to vet thousands of claims for universal credit payments across England found it incorrectly selected people from some groups more than others when recommending whom to investigate for possible fraud.

The admission was made in documents released under the Freedom of Information Act by the Department for Work and Pensions (DWP). The “statistically significant outcome disparity” emerged in a “fairness analysis” of the automated system for universal credit advances carried out in February this year.

The emergence of the bias comes after the DWP this summer claimed the AI system “does not present any immediate concerns of discrimination, unfair treatment or detrimental impact on customers”.

This assurance came in part because the final decision on whether a person gets a welfare payment is still made by a human, and officials believe the continued use of the system – which is attempting to help cut an estimated £8bn a year lost in fraud and error – is “reasonable and proportionate”.

But no fairness analysis has yet been undertaken in respect of potential bias centring on race, sex, sexual orientation and religion, or pregnancy, maternity and gender reassignment status, the disclosures reveal.

Campaigners responded by accusing the government of a “hurt first, fix later” policy and called on ministers to be more open about which groups were likely to be wrongly suspected by the algorithm of trying to cheat the system…(More)”.

Bad data costs Americans trillions. Let’s fix it with a renewed data strategy

Curated on Posted on by Stefaan Verhulst

Article by Nick Hart & Suzette Kent: “Over the past five years, the federal government lost $200-to-$500 billion per year in fraud to improper payments — that’s up to $3,000 taken from every working American’s pocket annually. Since 2003, these preventable losses have totaled an astounding $2.7 trillion. But here’s the good news: We already have the data and technology to greatly eliminate this waste in the years ahead. The operational structure and legal authority to put these tools to work protecting taxpayer dollars needs to be refreshed and prioritized.

The challenge is straightforward: Government agencies often can’t effectively share and verify basic information before sending payments. For example, federal agencies may not be able to easily check if someone is deceased, verify income or detect duplicate payments across programs…(More)”.

The British state is blind

Curated on Posted on by Stefaan Verhulst

The Economist: “Britiain is a bit bigger than it thought. In 2023 net migration stood at 906,000 people, rather more than the 740,000 previously estimated, according to the Office for National Statistics. It is equivalent to discovering an extra Slough. New numbers for 2022 also arrived. At first the ONS thought net migration stood at 606,000. Now it reckons the figure was 872,000, a difference roughly the size of Stoke-on-Trent, a small English city.

If statistics enable the state to see, then the British government is increasingly short-sighted. Fundamental questions, such as how many people arrive each year, are now tricky to answer. How many people are in work? The answer is fuzzy. Just how big is the backlog of court cases? The Ministry of Justice will not say, because it does not know. Britain is a blind state.

This causes all sorts of problems. The Labour Force Survey, once a gold standard of data collection, now struggles to provide basic figures. At one point the Resolution Foundation, an economic think-tank, reckoned the ONS had underestimated the number of workers by almost 1m since 2019. Even after the ONS rejigged its tally on December 3rd, the discrepancy is still perhaps 500,000, Resolution reckons. Things are so bad that Andrew Bailey, the governor of the Bank of England, makes jokes about the inaccuracy of Britain’s job-market stats in after-dinner speeches—akin to a pilot bursting out of the cockpit mid-flight and asking to borrow a compass, with a chuckle.

Sometimes the sums in question are vast. When the Department for Work and Pensions put out a new survey on household income in the spring, it was missing about £40bn ($51bn) of benefit income, roughly 1.5% of gdp or 13% of all welfare spending. This makes things like calculating the rate of child poverty much harder. Labour mps want this line to go down. Yet it has little idea where the line is to begin with.

Even small numbers are hard to count. Britain has a backlog of court cases. How big no one quite knows: the Ministry of Justice has not published any data on it since March. In the summer, concerned about reliability, it held back the numbers (which means the numbers it did publish are probably wrong, says the Institute for Government, another think-tank). And there is no way of tracking someone from charge to court to prison to probation. Justice is meant to be blind, but not to her own conduct…(More)”.

AI, huge hacks leave consumers facing a perfect storm of privacy perils

Curated on Posted on by Stefaan Verhulst

Article by Joseph Menn: “Hackers are using artificial intelligence to mine unprecedented troves of personal information dumped online in the past year, along with unregulated commercial databases, to trick American consumers and even sophisticated professionals into giving up control of bank and corporate accounts.

Armed with sensitive health information, calling records and hundreds of millions of Social Security numbers, criminals and operatives of countries hostile to the United States are crafting emails, voice calls and texts that purport to come from government officials, co-workers or relatives needing help, or familiar financial organizations trying to protect accounts instead of draining them.

“There is so much data out there that can be used for phishing and password resets that it has reduced overall security for everyone, and artificial intelligence has made it much easier to weaponize,” said Ashkan Soltani, executive director of the California Privacy Protection Agency, the only such state-level agency.

The losses reported to the FBI’s Internet Crime Complaint Center nearly tripled from 2020 to 2023, to $12.5 billion, and a number of sensitive breaches this year have only increased internet insecurity. The recently discovered Chinese government hacks of U.S. telecommunications companies AT&T, Verizon and others, for instance, were deemed so serious that government officials are being told not to discuss sensitive matters on the phone, some of those officials said in interviews. A Russian ransomware gang’s breach of Change Healthcare in February captured data on millions of Americans’ medical conditions and treatments, and in August, a small data broker, National Public Data, acknowledged that it had lost control of hundreds of millions of Social Security numbers and addresses now being sold by hackers.

Meanwhile, the capabilities of artificial intelligence are expanding at breakneck speed. “The risks of a growing surveillance industry are only heightened by AI and other forms of predictive decision-making, which are fueled by the vast datasets that data brokers compile,” U.S. Consumer Financial Protection Bureau Director Rohit Chopra said in September…(More)”.