Rejecting Public Utility Data Monopolies

Curated on Posted on by Stefaan Verhulst

Paper by Amy L. Stein: “The threat of monopoly power looms large today. Although not the telecommunications and tobacco monopolies of old, the Goliaths of Big Tech have become today’s target for potential antitrust violations. It is not only their control over the social media infrastructure and digital advertising technologies that gives people pause, but their monopolistic collection, use, and sale of customer data. But large technology companies are not the only private companies that have exclusive access to your data; that can crowd out competitors; and that can hold, use, or sell your data with little to no regulation. These other private companies are not data companies, platforms, or even brokers. They are public utilities.

Although termed “public utilities,” these entities are overwhelmingly private, shareholder-owned entities. Like private Big Tech, utilities gather incredible amounts of data from customers and use this data in various ways. And like private Big Tech, these utilities can exercise exclusionary and self-dealing anticompetitive behavior with respect to customer data. But there is one critical difference— unlike Big Tech, utilities enjoy an implied immunity from antitrust laws. This state action immunity has historically applied to utility provision of essential services like electricity and heat. As utilities find themselves in the position of unsuspecting data stewards, however, there is a real and unexplored question about whether their long- enjoyed antitrust immunity should extend to their data practices.

As the first exploration of this question, this Article tests the continuing application and rationale of the state action immunity doctrine to the evolving services that a utility provides as the grid becomes digitized. It demonstrates the importance of staunching the creep of state action immunity over utility data practices. And it recognizes the challenges of developing remedies for such data practices that do not disrupt the state-sanctioned monopoly powers of utilities over the provision of essential services. This Article analyzes both antitrust and regulatory remedies, including a new customer- focused “data duty,” as possible mechanisms to enhance consumer (ratepayer) welfare in this space. Exposing utility data practices to potential antitrust liability may be just the lever that is needed to motivate states, public utility commissions, and utilities to develop a more robust marketplace for energy data…(More)”.

This is AI’s brain on AI

Curated on Posted on by Stefaan Verhulst

Article by Alison Snyder Data to train AI models increasingly comes from other AI models in the form of synthetic data, which can fill in chatbots’ knowledge gaps but also destabilize them.

The big picture: As AI models expand in size, their need for data becomes insatiable — but high quality human-made data is costly, and growing restrictions on the text, images and other kinds of data freely available on the web are driving the technology’s developers toward machine-produced alternatives.

State of play: AI-generated data has been used for years to supplement data in some fields, including medical imaging and computer vision, that use proprietary or private data.

  • But chatbots are trained on public data collected from across the internet that is increasingly being restricted — while at the same time, the web is expected to be flooded with AI-generated content.

Those constraints and the decreasing cost of generating synthetic data are spurring companies to use AI-generated data to help train their models.

  • Meta, Google, Anthropic and others are using synthetic data — alongside human-generated data — to help train the AI models that power their chatbots.
  • Google DeepMind’s new AlphaGeometry 2 system that can solve math Olympiad problems is trained from scratch on synthetic data…(More)”

Generative Discrimination: What Happens When Generative AI Exhibits Bias, and What Can Be Done About It

Curated on Posted on by Stefaan Verhulst

Paper by Philipp Hacker, Frederik Zuiderveen Borgesius, Brent Mittelstadt and Sandra Wachter: “Generative AI (genAI) technologies, while beneficial, risk increasing discrimination by producing demeaning content and subtle biases through inadequate representation of protected groups. This chapter examines these issues, categorizing problematic outputs into three legal categories: discriminatory content; harassment; and legally hard cases like harmful stereotypes. It argues for holding genAI providers and deployers liable for discriminatory outputs and highlights the inadequacy of traditional legal frameworks to address genAI-specific issues. The chapter suggests updating EU laws to mitigate biases in training and input data, mandating testing and auditing, and evolving legislation to enforce standards for bias mitigation and inclusivity as technology advances…(More)”.

A.I. May Save Us, or May Construct Viruses to Kill Us

Curated on Posted on by Stefaan Verhulst

Article by Nicholas Kristof: “Here’s a bargain of the most horrifying kind: For less than $100,000, it may now be possible to use artificial intelligence to develop a virus that could kill millions of people.

That’s the conclusion of Jason Matheny, the president of the RAND Corporation, a think tank that studies security matters and other issues.

“It wouldn’t cost more to create a pathogen that’s capable of killing hundreds of millions of people versus a pathogen that’s only capable of killing hundreds of thousands of people,” Matheny told me.

In contrast, he noted, it could cost billions of dollars to produce a new vaccine or antiviral in response…

In the early 2000s, some of us worried about smallpox being reintroduced as a bioweapon if the virus were stolen from the labs in Atlanta and in Russia’s Novosibirsk region that retain the virus since the disease was eradicated. But with synthetic biology, now it wouldn’t have to be stolen.

Some years ago, a research team created a cousin of the smallpox virus, horse pox, in six months for $100,000, and with A.I. it could be easier and cheaper to refine the virus.

One reason biological weapons haven’t been much used is that they can boomerang. If Russia released a virus in Ukraine, it could spread to Russia. But a retired Chinese general has raised the possibility of biological warfare that targets particular races or ethnicities (probably imperfectly), which would make bioweapons much more useful. Alternatively, it might be possible to develop a virus that would kill or incapacitate a particular person, such as a troublesome president or ambassador, if one had obtained that person’s DNA at a dinner or reception.

Assessments of ethnic-targeting research by China are classified, but they may be why the U.S. Defense Department has said that the most important long-term threat of biowarfare comes from China.

A.I. has a more hopeful side as well, of course. It holds the promise of improving education, reducing auto accidents, curing cancers and developing miraculous new pharmaceuticals.

One of the best-known benefits is in protein folding, which can lead to revolutionary advances in medical care. Scientists used to spend years or decades figuring out the shapes of individual proteins, and then a Google initiative called AlphaFold was introduced that could predict the shapes within minutes. “It’s Google Maps for biology,” Kent Walker, president of global affairs at Google, told me.

Scientists have since used updated versions of AlphaFold to work on pharmaceuticals including a vaccine against malaria, one of the greatest killers of humans throughout history.

So it’s unclear whether A.I. will save us or kill us first…(More)”.

Future-proofing government data

Curated on Posted on by Stefaan Verhulst

Article by Amy Jones: “Vast amounts of data are fueling innovation and decision-making, and agencies representing the United States government are custodian to some of the largest repositories of data in the world. As one of the world’s largest data creators and consumers, the federal government has made substantial investments in sourcing, curating, and leveraging data across many domains. However, the increasing reliance on artificial intelligence to extract insights and drive efficiencies necessitates a strategic pivot: agencies must evolve data management practices to identify and discriminate synthetic data from organic sources to safeguard the integrity and utility of data assets.

AI’s transformative potential is contingent on the availability of high-quality data. Data readiness includes attention to quality, accuracy, completeness, consistency, timeliness and relevance, at a minimum, and agencies are adopting robust data governance frameworks that enforce data quality standards at every stage of the data lifecycle. This includes implementing advanced data validation techniques, fostering a culture of data stewardship, and leveraging state-of-the-art tools for continuous data quality monitoring…(More)”.

Supporting Scientific Citizens

Curated on Posted on by Stefaan Verhulst

Article by Lisa Margonelli: “What do nuclear fusion power plants, artificial intelligence, hydrogen infrastructure, and drinking water recycled from human waste have in common? Aside from being featured in this edition of Issues, they all require intense public engagement to choose among technological tradeoffs, safety profiles, and economic configurations. Reaching these understandings requires researchers, engineers, and decisionmakers who are adept at working with the public. It also requires citizens who want to engage with such questions and can articulate what they want from science and technology.

This issue offers a glimpse into what these future collaborations might look like. To train engineers with the “deep appreciation of the social, cultural, and ethical priorities and implications of the technological solutions engineers are tasked with designing and deploying,” University of Michigan nuclear engineer Aditi Verma and coauthors Katie Snyder and Shanna Daly asked their first-year engineering students to codesign nuclear power plants in collaboration with local community members. Although traditional nuclear engineering classes avoid “getting messy,” Verma and colleagues wanted students to engage honestly with the uncertainties of the profession. In the process of working with communities, the students’ vocabulary changed; they spoke of trust, respect, and “love” for community—even when considering deep geological waste repositories…(More)”.

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)”.