Governments Empower Citizens by Promoting Digital Rights

Curated on Posted on by Stefaan Verhulst

Article by Julia Edinger: “The rapid rise of digital services and smart city technology has elevated concerns about privacy in the digital age and government’s role, even as cities from California to Texas take steps to make constituents aware of their digital rights.

Earlier this month, Long Beach, Calif., launched an improved version of its Digital Rights Platform, which shows constituents their data privacy and digital rights and information about how the city uses technologies while protecting digital rights.

“People’s digital rights are no different from their human or civil rights, except that they’re applied to how they interact with digital technologies — when you’re online, you’re still entitled to every right you enjoy offline,” said Will Greenberg, staff technologist at the Electronic Frontier Foundation (EFF), in a written statement. The nonprofit organization defends civil liberties in the digital world.


Long Beach’s platform initially launched several years ago, to mitigate privacy concerns that came out of the 2020 launch of a smart city initiative, according to Long Beach CIO Lea Eriksen. When that initiative debuted, the Department of Innovation and Technology requested the City Council approve a set of data privacy guidelines to ensure digital rights would be protected, setting the stage for the initial platform launch. Its 2021 beta version has now been enhanced to offer information on 22 city technology uses, up from two, and an enhanced feedback module enabling continued engagement and platform improvements…(More)”.

Harnessing Technology for Inclusive Prosperity

Curated on Posted on by Stefaan Verhulst

Book edited by Brahima Sangafowa Coulibaly and Zia Qureshi: “Transformative new technologies are reshaping economies and societies. But as they create new opportunities, they also pose new challenges, not least of which is rising inequality. Increased disparities and related anxieties are stoking societal discontent and political ferment. Harnessing technological transformation in ways that foster its benefits, contain risks, and build inclusive prosperity is a major public policy challenge of our time and one that motivates this book.

In what ways are the new technologies altering markets, business models, work, and, in turn, economic growth and income distribution? How are they affecting inequality within advanced and emerging economies and the prospects for economic convergence between them? What are the implications for public policy? What new thinking and adaptations are needed to realign institutions and policies, at national and global levels, with the new dynamics of the digital era?

This book addresses these questions. It seeks to promote ideas and actions to manage digital transformation and the latest advances in artificial intelligence with foresight and purpose to shape a more prosperous and inclusive future…(More)”

Is peer review failing its peer review?

Curated on Posted on by Stefaan Verhulst

Article by First Principles: “Ivan Oransky doesn’t sugar-coat his answer when asked about the state of academic peer review: “Things are pretty bad.”

As a distinguished journalist in residence at New York University and co-founder of Retraction Watch – a site that chronicles the growing number of papers being retracted from academic journals – Oransky is better positioned than just about anyone to make such a blunt assessment. 

He elaborates further, citing a range of factors contributing to the current state of affairs. These include the publish-or-perish mentality, chatbot ghostwriting, predatory journals, plagiarism, an overload of papers, a shortage of reviewers, and weak incentives to attract and retain reviewers.

“Things are pretty bad and they have been bad for some time because the incentives are completely misaligned,” Oranksy told FirstPrinciples in a call from his NYU office. 

Things are so bad that a new world record was set in 2023: more than 10,000 research papers were retracted from academic journals. In a troubling development, 19 journals closed after being inundated by a barrage of fake research from so-called “paper mills” that churn out the scientific equivalent of clickbait, and one scientist holds the current record of 213 retractions to his name. 

“The numbers don’t lie: Scientific publishing has a problem, and it’s getting worse,” Oransky and Retraction Watch co-founder Adam Marcus wrote in a recent opinion piece for The Washington Post. “Vigilance against fraudulent or defective research has always been necessary, but in recent years the sheer amount of suspect material has threatened to overwhelm publishers.”..(More)”.

The problem of ‘model collapse’: how a lack of human data limits AI progress

Curated on Posted on by Stefaan Verhulst

Article by Michael Peel: “The use of computer-generated data to train artificial intelligence models risks causing them to produce nonsensical results, according to new research that highlights looming challenges to the emerging technology. 

Leading AI companies, including OpenAI and Microsoft, have tested the use of “synthetic” data — information created by AI systems to then also train large language models (LLMs) — as they reach the limits of human-made material that can improve the cutting-edge technology.

Research published in Nature on Wednesday suggests the use of such data could lead to the rapid degradation of AI models. One trial using synthetic input text about medieval architecture descended into a discussion of jackrabbits after fewer than 10 generations of output. 

The work underlines why AI developers have hurried to buy troves of human-generated data for training — and raises questions of what will happen once those finite sources are exhausted. 

“Synthetic data is amazing if we manage to make it work,” said Ilia Shumailov, lead author of the research. “But what we are saying is that our current synthetic data is probably erroneous in some ways. The most surprising thing is how quickly this stuff happens.”

The paper explores the tendency of AI models to collapse over time because of the inevitable accumulation and amplification of mistakes from successive generations of training.

The speed of the deterioration is related to the severity of shortcomings in the design of the model, the learning process and the quality of data used. 

The early stages of collapse typically involve a “loss of variance”, which means majority subpopulations in the data become progressively over-represented at the expense of minority groups. In late-stage collapse, all parts of the data may descend into gibberish…(More)”.

Citizens should be asked to do more

Curated on Posted on by Stefaan Verhulst

Article by Martin Wolf: “In an excellent “Citizens’ White Paper”, in partnership with participation charity Involve, Demos describes the needed revolution as follows, “We don’t just need new policies for these challenging times. We need new ways to tackle the policy challenges we face — from national missions to everyday policymaking. We need new ways to understand and negotiate what the public will tolerate. We need new ways to build back trust in politicians”. In sum, it states, “if government wants to be trusted by the people, it must itself start to trust the people.”

Bar chart of agreement that public should be involved in decision making on these issues (%) showing the public has clear ideas on where it should be most involved

The fundamental aim is to change the perception of government from something that politicians and bureaucrats do to us into an activity that involves not everyone, which is impossible, but ordinary people selected by lot. This, as I have noted, would be the principle of the jury imported into public life.

How might this work? The idea is to select representative groups of ordinary people affected by policies into official discussion on problems and solutions. This could be at the level of central, devolved or local government. The participants would not just be asked for opinions, but be actively engaged in considering issues and shaping (though not making) decisions upon them. The paper details a number of different approaches — panels, assemblies, juries, workshops and wider community conversations. Which would be appropriate would depend on the task…(More)”.

Illuminating ‘the ugly side of science’: fresh incentives for reporting negative results

Curated on Posted on by Stefaan Verhulst

Article by Rachel Brazil: “Editor-in-chief Sarahanne Field describes herself and her team at the Journal of Trial & Error as wanting to highlight the “ugly side of science — the parts of the process that have gone wrong”.

She clarifies that the editorial board of the journal, which launched in 2020, isn’t interested in papers in which “you did a shitty study and you found nothing. We’re interested in stuff that was done methodologically soundly, but still yielded a result that was unexpected.” These types of result — which do not prove a hypothesis or could yield unexplained outcomes — often simply go unpublished, explains Field, who is also an open-science researcher at the University of Groningen in the Netherlands. Along with Stefan Gaillard, one of the journal’s founders, she hopes to change that.

Calls for researchers to publish failed studies are not new. The ‘file-drawer problem’ — the stacks of unpublished, negative results that most researchers accumulate — was first described in 1979 by psychologist Robert Rosenthal. He argued that this leads to publication bias in the scientific record: the gap of missing unsuccessful results leads to overemphasis on the positive results that do get published…(More)”.

When A.I. Fails the Language Test, Who Is Left Out of the Conversation?

Curated on Posted on by Stefaan Verhulst

Article by Sara Ruberg: “While the use of A.I. has exploded in the West, much of the rest of the world has been left out of the conversation since most of the technology is trained in English. A.I. experts worry that the language gap could exacerbate technological inequities, and that it could leave many regions and cultures behind.

A delay of access to good technology of even a few years, “can potentially lead to a few decades of economic delay,” said Sang Truong, a Ph.D. candidate at the Stanford Artificial Intelligence Laboratory at Stanford University on the team that built and tested a Vietnamese language model against others.

The tests his team ran found that A.I. tools across the board could get facts and diction wrong when working with Vietnamese, likely because it is a “low-resource” language by industry standards, which means that there aren’t sufficient data sets and content available online for the A.I. model to learn from.

Low-resource languages are spoken by tens and sometimes hundreds of millions of people around the world, but they yield less digital data because A.I. tech development and online engagement is centered in the United States and China. Other low-resource languages include Hindi, Bengali and Swahili, as well as lesser-known dialects spoken by smaller populations around the world.

An analysis of top websites by W3Techs, a tech survey company, found that English makes up over 60 percent of the internet’s language data. While English is widely spoken globally, native English speakers make up about 5 percent of the population, according to Ethnologue, a research organization that collects language data. Mandarin and Spanish are other examples of languages with a significant online presence and reliable digital data sets.

Academic institutions, grass-roots organizations and volunteer efforts are playing catch-up to build resources for speakers of languages who aren’t as well represented in the digital landscape.

Lelapa AI, based in Johannesburg, is one such company leading efforts on the African continent. The South African-based start-up is developing multilingual A.I. products for people and businesses in Africa…(More)”.

Feeding the Machine: The Hidden Human Labor Powering A.I.

Curated on Posted on by Stefaan Verhulst

Book by Mark Graham, Callum Cant, and James Muldoon: “Silicon Valley has sold us the illusion that artificial intelligence is a frictionless technology that will bring wealth and prosperity to humanity. But hidden beneath this smooth surface lies the grim reality of a precarious global workforce of millions laboring under often appalling conditions to make A.I. possible. This book presents an urgent, riveting investigation of the intricate network that maintains this exploitative system, revealing the untold truth of A.I.

Based on hundreds of interviews and thousands of hours of fieldwork over more than a decade, Feeding the Machine describes the lives of the workers deliberately concealed from view, and the power structures that determine their future. It gives voice to the people whom A.I. exploits, from accomplished writers and artists to the armies of data annotators, content moderators and warehouse workers, revealing how their dangerous, low-paid labor is connected to longer histories of gendered, racialized, and colonial exploitation.

A.I. is an extraction machine that feeds off humanity’s collective effort and intelligence, churning through ever-larger datasets to power its algorithms. This book is a call to arms that details what we need to do to fight for a more just digital future…(More)”.

AI firms will soon exhaust most of the internet’s data

Curated on Posted on by Stefaan Verhulst

Article by The Economist: “One approach is to focus on data quality rather than quantity. ai labs do not simply train their models on the entire internet. They filter and sequence data to maximise how much their models learn. Naveen Rao of Databricks, an ai firm, says that this is the “main differentiator” between ai models on the market. “True information” about the world obviously matters; so does lots of “reasoning”. That makes academic textbooks, for example, especially valuable. But setting the balance between data sources remains something of a dark art. What is more, the ordering in which the system encounters different types of data matters too. Lump all the data on one topic, like maths, at the end of the training process, and your model may become specialised at maths but forget some other concepts.

These considerations can get even more complex when the data are not just on different subjects but in different forms. In part because of the lack of new textual data, leading models like Openai’s gpt-4o and Google’s Gemini are now let loose on image, video and audio files as well as text during their self-supervised learning. Training on video is hardest given how dense with data points video files are. Current models typically look at a subset of frames to simplify things.

Whatever models are used, ownership is increasingly recognised as an issue. The material used in training llms is often copyrighted and used without consent from, or payment to, the rights holders. Some ai models peep behind paywalls. Model creators claim this sort of thing falls under the “fair use” exemption in American copyright law. ai models should be allowed to read copyrighted material when they learn, just as humans can, they say. But as Benedict Evans, a technology analyst, has put it, “a difference in scale” can lead to “a difference in principle”…

It is clear that access to more data—whether culled from specialist sources, generated synthetically or provided by human experts—is key to maintaining rapid progress in ai. Like oilfields, the most accessible data reserves have been depleted. The challenge now is to find new ones—or sustainable alternatives…(More)”.

Rethinking Dual-Use Technology

Curated on Posted on by Stefaan Verhulst

Article by Artur Kluz and Stefaan Verhulst: “A new concept of “triple use” — where technology serves commercial, defense, and peacebuilding purposes — may offer a breakthrough solution for founders, investors and society to explore….

As a result of the resurgence of geopolitical tensions, the debate about the applications of dual-use technology is intensifying. The core issue founders, tech entrepreneurs, venture capitalists (VCs), and limited partner investors (LPs) are examining is whether commercial technologies should increasingly be re-used for military purposes. Traditionally, the majority of  investors (including limited partners) have prohibited dual-use tech in their agreements. However, the rapidly growing dual-use market, with its substantial addressable size and growth potential, is compelling all stakeholders to reconsider this stance. The pressure for innovations, capital returns and Return On Investment (ROI) is driving the need for a solution. 

These discussions are fraught with moral complexity, but they also present an opportunity to rethink the dual-use paradigm and foster investment in technologies aimed at supporting peace. A new concept of “triple use”— where technology serves commercial, defense, and peacebuilding purposes — may offer an innovative and more positive avenue for founders, investors and society to explore. This additional re-use, which remains in an incipient state, is increasingly being referred to as PeaceTech. By integrating terms dedicated to PeaceTech in new and existing investment and LP agreements, tech companies, founders and venture capital investors can be also required to apply their technology for peacebuilding purposes. This approach can expand the applications of emerging technologies to also include conflict prevention, reconstruction or any humanitarian aspects.

However, current efforts to use technologies for peacebuilding are impeded by various obstacles, including a lack of awareness within the tech sector and among investors, limited commercial interest, disparities in technical capacity, privacy concerns, international relations and political complexities. In the below we examine some of these challenges, while also exploring certain avenues for overcoming them — including approaching technologies for peace as a “triple use” application. We will especially try to identify examples of how tech companies, tech entrepreneurs, accelerators, and tech investors including VCs and LPs can commercially benefit and support “triple use” technologies. Ultimately, we argue, the vast potential — largely untapped — of “triple use” technologies calls for a new wave of tech ecosystem transformation and public and private investments as well as the development of a new field of research…(More)”.