The New Digital Dark Age


Article by Gina Neff: “For researchers, social media has always represented greater access to data, more democratic involvement in knowledge production, and great transparency about social behavior. Getting a sense of what was happening—especially during political crises, major media events, or natural disasters—was as easy as looking around a platform like Twitter or Facebook. In 2024, however, that will no longer be possible.

In 2024, we will face a grim digital dark age, as social media platforms transition away from the logic of Web 2.0 and toward one dictated by AI-generated content. Companies have rushed to incorporate large language models (LLMs) into online services, complete with hallucinations (inaccurate, unjustified responses) and mistakes, which have further fractured our trust in online information.

Another aspect of this new digital dark age comes from not being able to see what others are doing. Twitter once pulsed with publicly readable sentiment of its users. Social researchers loved Twitter data, relying on it because it provided a ready, reasonable approximation of how a significant slice of internet users behaved. However, Elon Musk has now priced researchers out of Twitter data after recently announcing that it was ending free access to the platform’s API. This made it difficult, if not impossible, to obtain data needed for research on topics such as public health, natural disaster response, political campaigning, and economic activity. It was a harsh reminder that the modern internet has never been free or democratic, but instead walled and controlled.

Closer cooperation with platform companies is not the answer. X, for instance, has filed a suit against independent researchers who pointed out the rise in hate speech on the platform. Recently, it has also been revealed that researchers who used Facebook and Instagram’s data to study the platforms’ role in the US 2020 elections had been granted “independence by permission” by Meta. This means that the company chooses which projects to share its data with and, while the research may be independent, Meta also controls what types of questions are asked and who asks them…(More)”.

What It Takes to Build Democratic Institutions


Article by Daron Acemoglu: “Chile’s failure to draft a new constitution that enjoys widespread support from voters is the predictable result of allowing partisans and ideologues to lead the process. Democratic institutions are built by delivering what ordinary voters expect and demand from government, as the history of Nordic social democracy shows…

There are plenty of good models around to help both developing and industrialized countries build better democratic institutions. But with its abortive attempts to draft a new constitution, Chile is offering a lesson in what to avoid.

Though it is one of the richest countries in Latin America, Chile is still suffering from the legacy of General Augusto Pinochet’s brutal dictatorship and historic inequalities. The country has made some progress in building democratic institutions since the 1988 plebiscite that began the transition from authoritarianism, and education and social programs have reduced income inequality. But major problems remain. There are deep inequalities not just in income, but also in access to government services, high-quality educational resources, and labor-market opportunities. Moreover, Chile still has the constitution that Pinochet imposed in 1980.

Yet while it seems natural to start anew, Chile has gone about it the wrong way. Following a 2020 referendum that showed overwhelming support for drafting a new constitution, it entrusted the process to a convention of elected delegates. But only 43% of voters turned out for the 2021 election to fill the convention, and many of the candidates were from far-left circles with strong ideological commitments to draft a constitution that would crack down on business and establish myriad new rights for different communities. When the resulting document was put to a vote, 62% of Chileans rejected it…(More)”

Toward a Solid Acceptance of the Decentralized Web of Personal Data: Societal and Technological Convergence


Article by Ana Pop Stefanija et al: “Citizens using common online services such as social media, health tracking, or online shopping effectively hand over control of their personal data to the service providers—often large corporations. The services using and processing personal data are also holding the data. This situation is problematic, as has been recognized for some time: competition and innovation are stifled; data is duplicated; and citizens are in a weak position to enforce legal rights such as access, rectification, or erasure. The approach to address this problem has been to ascertain that citizens can access and update, with every possible service provider, the personal data that providers hold of or about them—the foundational view taken in the European General Data Protection Regulation (GDPR).

Recently, however, various societal, technological, and regulatory efforts are taking a very different approach, turning things around. The central tenet of this complementary view is that citizens should regain control of their personal data. Once in control, citizens can decide which providers they want to share data with, and if so, exactly which part of their data. Moreover, they can revisit these decisions anytime…(More)”.

What does it mean to trust a technology?


Article by Jack Stilgoe: “A survey published in October 2023 revealed what seemed to be a paradox. Over the past decade, self-driving vehicles have improved immeasurably, but public trust in the technology is low and falling. Only 37% of Americans said they would be comfortable riding in a self- driving vehicle, down from 39% in 2022 and 41% in 2021. Those that have used the technology express more enthusiasm, but the rest have seemingly had their confidence shaken by the failure of the technology to live up to its hype.

Purveyors and regulators of any new technology are likely to worry about public trust. In the short term, they worry that people won’t want to make use of new innovations. But they also worry that a public backlash might jeopardize not just a single company but a whole area of technological innovation. Excitement about artificial intelligence (AI) has been accompanied by a concern about the need to “build trust” in the technology. Trust—letting one’s guard down despite incomplete information—is vital, but innovators must not take it for granted. Nor can it be circumvented through clever engineering. When cryptocurrency enthusiasts call their technology “trustless” because they think it solves age-old problems of banking (an unavoidably imperfect social institution), we should at least view them with skepticism.

For those concerned about public trust and new technologies, social science has some important lessons. The first is that people trust people, not things. When we board an airplane or agree to get vaccinated, we are placing our trust not in these objects but in the institutions that govern them. We trust that professionals are well-trained; we trust that regulators have assessed the risks; we trust that, if something goes wrong, someone will be held accountable, harms will be compensated, and mistakes will be rectified. Societies can no longer rely on the face-to-face interactions that once allowed individuals to do business. So it is more important than ever that faceless institutions are designed and continuously monitored to realize the benefits of new technologies while mitigating the risks….(More)”.

How to craft fair, transparent data-sharing agreements


Article by Stephanie Kanowitz: “Data collaborations are critical to government decision-making, but actually sharing data can be difficult—not so much the mechanics of the collaboration, but hashing out the rules and policies governing it. A new report offers three resources that will make data sharing more straightforward, foster accountability and build trust among the parties.

“We’ve heard over and over again that one of the biggest barriers to collaboration around data turns out to be data sharing agreements,” said Stefaan Verhulst, co-founder of the Governance Lab at New York University and an author of the November report, “Moving from Idea to Practice.” It’s sometimes a lot to ask stakeholders “to provide access to some of their data,” he said.

To help, Verhulst and other researchers identified three components of successful data-sharing agreements: conducting principled negotiations, establishing the elements of a data-sharing agreement and assessing readiness.

To address the first, the report breaks the components of negotiation into a framework with four tenets: separating people from the problem, focusing on interests rather than positions, identifying options and using objective criteria. From discussions with stakeholders in data sharing agreement workshops that GovLab held through its Open Data Policy Lab, three principles emerged—fairness, transparency and reciprocity…(More)”.

The new star wars over satellites


Article by Peggy Hollinger: “There is a battle brewing in space. In one corner you have the billionaires building giant satellite broadband constellations in low earth orbit (LEO) — Elon Musk with SpaceX’s Starlink and Jeff Bezos with Project Kuiper. 

In the other corner stand the traditional fixed satellite operators such as ViaSat and SES — but also a number of nations increasingly uncomfortable with the way in which the new space economy is evolving. In other words, with the dominance of US mega constellations in a strategic region of space.

The first shots were fired in late November at the World Radiocommunications Conference in Dubai. Every four years, global regulators and industry meet to review international regulations on the use of radio spectrum. 

For those who have only a vague idea of what spectrum is, it is the name for the radio airwaves that carry data wirelessly to enable a vast range of services — from television broadcasting to WiFi, navigation to mobile communications.

Most people are inclined to think that the airwaves have infinite capacity to connect us. But, like water, spectrum is a finite resource and much of it has already been allocated to specific uses. So operators have to transmit signals on shared bands of spectrum — on the promise that their transmissions will not interfere with others. 

Now SpaceX, Kuiper and others operating in LEO are pushing to loosen rules designed to prevent their signals from interfering with those of traditional operators in higher orbits. These rules impose caps on the power used to transmit signals, which facilitate spectrum sharing but also constrain the amount of data they can send. LEO operators say the rules, designed 25 years ago, are outdated. They argue that new technology would allow higher power levels — and greater capacity for customers — without degrading networks of the traditional fixed satellite systems operating in geostationary orbit, at altitudes of 36,000km.

It is perhaps not a surprise that a proposal to make LEO constellations more competitive drew protests from geo operators. Some, such as US-based Hughes Network Systems, have admitted they are already losing customers to Starlink.

What was surprising, however, was the strong opposition from countries such as Brazil, Indonesia, Japan and others…(More)”.

How Tracking and Technology in Cars Is Being Weaponized by Abusive Partners


Article by Kashmir Hill: “After almost 10 years of marriage, Christine Dowdall wanted out. Her husband was no longer the charming man she had fallen in love with. He had become narcissistic, abusive and unfaithful, she said. After one of their fights turned violent in September 2022, Ms. Dowdall, a real estate agent, fled their home in Covington, La., driving her Mercedes-Benz C300 sedan to her daughter’s house near Shreveport, five hours away. She filed a domestic abuse report with the police two days later.

Her husband, a Drug Enforcement Administration agent, didn’t want to let her go. He called her repeatedly, she said, first pleading with her to return, and then threatening her. She stopped responding to him, she said, even though he texted and called her hundreds of times.

Ms. Dowdall, 59, started occasionally seeing a strange new message on the display in her Mercedes, about a location-based service called “mbrace.” The second time it happened, she took a photograph and searched for the name online.

“I realized, oh my God, that’s him tracking me,” Ms. Dowdall said.

“Mbrace” was part of “Mercedes me” — a suite of connected services for the car, accessible via a smartphone app. Ms. Dowdall had only ever used the Mercedes Me app to make auto loan payments. She hadn’t realized that the service could also be used to track the car’s location. One night, when she visited a male friend’s home, her husband sent the man a message with a thumbs-up emoji. A nearby camera captured his car driving in the area, according to the detective who worked on her case.

Ms. Dowdall called Mercedes customer service repeatedly to try to remove her husband’s digital access to the car, but the loan and title were in his name, a decision the couple had made because he had a better credit score than hers. Even though she was making the payments, had a restraining order against her husband and had been granted sole use of the car during divorce proceedings, Mercedes representatives told her that her husband was the customer so he would be able to keep his access. There was no button she could press to take away the app’s connection to the vehicle.

“This is not the first time that I’ve heard something like this,” one of the representatives told Ms. Dowdall…(More)”.

The Rise of Cyber-Physical Systems


Article by Chandrakant D. Patel: “Cyber-physical systems are a systemic integration of physical and cyber technologies. To name one example, a self-driving car is an integration of physical technologies, such as motors, batteries, actuators, and sensors, and cyber technologies, like communication, computation, inference, and closed-loop control. Data flow from physical to cyber technologies results in systemic integration and the desired driving experience. Cyber-physical systems are becoming prevalent in a range of sectors, such as power, water, waste, transportation, healthcare, agriculture, and manufacturing. We have entered the cyber-physical age. However, we stand unprepared for this moment due to systemic under-allocation in the physical sciences and the lack of a truly multidisciplinary engineering curriculum.  While there are many factors that contribute to the rise of cyber-physical systems, societal challenges stemming from imbalances between supply and demand are becoming a very prominent one. These imbalances are caused by social, economic, and ecological trends that hamper the delivery of basic goods and services. Examples of trends leading to imbalances between supply and demand are resource constraints, aging population, human capital constraints, a lack of subject matter experts in critical fields, physical security risks, supply-chain and supply-side resiliency, and externalities such as pandemics and environmental pollution. With respect to the lack of subject matter experts, consider the supply of cardiothoracic surgeons. The United States has about 4000 cardiothoracic surgeons, a sub-specialization that takes 20 years of education and hands-on training, for a population of 333 million. Similar imbalances in subject matter experts in healthcare, power, water, waste, and transport systems are occurring as a result of aging population. Compounding this challenge is the market-driven pay discrepancy that has attracted our youth to software jobs, such as those in social media, which pay much more relative to the salaries for a resident in general surgery or an early-career civil engineer. While it is possible that the market will shift to value infrastructure- and healthcare-related jobs, the time it takes to train “hands-on” contributors (e.g., engineers and technicians) in physical sciences and life sciences is substantial, ranging from 5 years (technicians requiring industry training) to 20 years (sub-specialized personnel like cardiothoracic surgeons)…(More)”.

Where Did the Open Access Movement Go Wrong?


An Interview with Richard Poynder by Richard Anderson: “…Open access was intended to solve three problems that have long blighted scholarly communication – the problems of accessibilityaffordability, and equity. 20+ years after the Budapest Open Access Initiative (BOAI) we can see that the movement has signally failed to solve the latter two problems. And with the geopolitical situation deteriorating solving the accessibility problem now also looks to be at risk. The OA dream of “universal open access” remains a dream and seems likely to remain one.

What has been the essence of the OA movement’s failure?

The fundamental problem was that OA advocates did not take ownership of their own movement. They failed, for instance, to establish a central organization (an OA foundation, if you like) in order to organize and better manage the movement; and they failed to publish a single, canonical definition of open access. This is in contrast to the open source movement, and is an omission I drew attention to in 2006

This failure to take ownership saw responsibility for OA pass to organizations whose interests are not necessarily in sync with the objectives of the movement.

It did not help that the BOAI definition failed to specify that to be classified as open access, scholarly works needed to be made freely available immediately on publication and that they should remain freely available in perpetuity. Nor did it give sufficient thought to how OA would be funded (and OA advocates still fail to do that).

This allowed publishers to co-opt OA for their own purposes, most notably by introducing embargoes and developing the pay-to-publish gold OA model, with its now infamous article processing charge (APC).

Pay-to-publish OA is now the dominant form of open access and looks set to increase the cost of scholarly publishing and so worsen the affordability problem. Amongst other things, this has disenfranchised unfunded researchers and those based in the global south (notwithstanding APC waiver promises).

What also did not help is that OA advocates passed responsibility for open access over to universities and funders. This was contradictory, because OA was conceived as something that researchers would opt into. The assumption was that once the benefits of open access were explained to them, researchers would voluntarily embrace it – primarily by self-archiving their research in institutional or preprint repositories. But while many researchers were willing to sign petitions in support of open access, few (outside disciplines like physics) proved willing to practice it voluntarily.

In response to this lack of engagement, OA advocates began to petition universities, funders, and governments to introduce OA policies recommending that researchers make their papers open access. When these policies also failed to have the desired effect, OA advocates demanded their colleagues be forced to make their work OA by means of mandates requiring them to do so.

Most universities and funders (certainly in the global north) responded positively to these calls, in the belief that open access would increase the pace of scientific development and allow them to present themselves as forward-thinking, future-embracing organizations. Essentially, they saw it as a way of improving productivity and ROI while enhancing their public image.

While many researchers were willing to sign petitions in support of open access, few proved willing to practice it voluntarily.

But in light of researchers’ continued reluctance to make their works open access, universities and funders began to introduce increasingly bureaucratic rules, sanctions, and reporting tools to ensure compliance, and to manage the more complex billing arrangements that OA has introduced.

So, what had been conceived as a bottom-up movement founded on principles of voluntarism morphed into a top-down system of command and control, and open access evolved into an oppressive bureaucratic process that has failed to address either the affordability or equity problems. And as the process, and the rules around that process, have become ever more complex and oppressive, researchers have tended to become alienated from open access.

As a side benefit for universities and funders OA has allowed them to better micromanage their faculty and fundees, and to monitor their publishing activities in ways not previously possible. This has served to further proletarianize researchers and today they are becoming the academic equivalent of workers on an assembly line. Philip Mirowski has predicted that open access will lead to the deskilling of academic labor. The arrival of generative AI might seem to make that outcome the more likely…

Can these failures be remedied by means of an OA reset? With this aim in mind (and aware of the failures of the movement), OA advocates are now devoting much of their energy to trying to persuade universities, funders, and philanthropists to invest in a network of alternative nonprofit open infrastructures. They envisage these being publicly owned and focused on facilitating a flowering of new diamond OA journals, preprint servers, and Publish, Review, Curate (PRC) initiatives. In the process, they expect commercial publishers will be marginalized and eventually dislodged.

But it is highly unlikely that the large sums of money that would be needed to create these alternative infrastructures will be forthcoming, certainly not at sufficient levels or on anything other than a temporary basis.

While it is true that more papers and preprints are being published open access each year, I am not convinced this is taking us down the road to universal open access, or that there is a global commitment to open access.

Consequently, I do not believe that a meaningful reset is possible: open access has reached an impasse and there is no obvious way forward that could see the objectives of the OA movement fulfilled.

Partly for this reason, we are seeing attempts to rebrand, reinterpret, and/or reimagine open access and its objectives…(More)”.

Rebalancing AI


Article by Daron Acemoglu and Simon Johnson: “Optimistic forecasts regarding the growth implications of AI abound. AI adoption could boost productivity growth by 1.5 percentage points per year over a 10-year period and raise global GDP by 7 percent ($7 trillion in additional output), according to Goldman Sachs. Industry insiders offer even more excited estimates, including a supposed 10 percent chance of an “explosive growth” scenario, with global output rising more than 30 percent a year.

All this techno-optimism draws on the “productivity bandwagon”: a deep-rooted belief that technological change—including automation—drives higher productivity, which raises net wages and generates shared prosperity.

Such optimism is at odds with the historical record and seems particularly inappropriate for the current path of “just let AI happen,” which focuses primarily on automation (replacing people). We must recognize that there is no singular, inevitable path of development for new technology. And, assuming that the goal is to sustainably improve economic outcomes for more people, what policies would put AI development on the right path, with greater focus on enhancing what all workers can do?…(More)”