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)”

What Will AI Do to Elections?


Article by Rishi Iyengar: “…Requests to X’s press team on how the platform was preparing for elections in 2024 yielded an automated response: “Busy now, please check back later”—a slight improvement from the initial Musk-era change where the auto-reply was a poop emoji.

X isn’t the only major social media platform with fewer content moderators. Meta, which owns Facebook, Instagram, and WhatsApp, has laid off more than 20,000 employees since November 2022—several of whom worked on trust and safety—while many YouTube employees working on misinformation policy were impacted by layoffs at parent company Google.

There could scarcely be a worse time to skimp on combating harmful content online. More than 50 countries, including the world’s three biggest democracies and Taiwan, an increasingly precarious geopolitical hot spot, are expected to hold national elections in 2024. Seven of the world’s 10 most populous countries—Bangladesh, India, Indonesia, Mexico, Pakistan, Russia, and the United States—will collectively send a third of the world’s population to the polls.

Elections, with their emotionally charged and often tribal dynamics, are where misinformation missteps come home to roost. If social media misinformation is the equivalent of yelling “fire” in a crowded theater, election misinformation is like doing so when there’s a horror movie playing and everyone’s already on edge.

Katie Harbath prefers a different analogy, one that illustrates how nebulous and thorny the issues are and the sheer uncertainty surrounding them. “The metaphor I keep using is a kaleidoscope because there’s so many different aspects to this but depending how you turn the kaleidoscope, the pattern changes of what it’s going to look like,” she said in an interview in October. “And that’s how I feel about life post-2024. … I don’t know where in the kaleidoscope it’s going to land.”

Harbath has become something of an election whisperer to the tech industry, having spent a decade at Facebook from 2011 building the company’s election integrity efforts from scratch. She left in 2021 and founded Anchor Change, a public policy consulting firm that helps other platforms combat misinformation and prepare for elections in particular.

Had she been in her old job, Harbath said, her team would have completed risk assessments of global elections by late 2022 or early 2023 and then spent the rest of the year tailoring Meta’s products to them as well as setting up election “war rooms” where necessary. “Right now, we would be starting to move into execution mode.” She cautions against treating the resources that companies are putting into election integrity as a numbers game—“once you build some of those tools, maintaining them doesn’t take as many people”—but acknowledges that the allocation of resources reveals a company leadership’s priorities.

The companies insist they remain committed to election integrity. YouTube has “heavily invested in the policies and systems that help us successfully support elections around the world,” spokesperson Ivy Choi said in a statement. TikTok said it has a total of 40,000 safety professionals and works with 16 fact-checking organizations across 50 global languages. Meta declined to comment for this story, but a company representative directed Foreign Policy to a recent blog post by Nick Clegg, a former U.K. deputy prime minister who now serves as Meta’s head of global affairs. “We have around 40,000 people working on safety and security, with more than $20 billion invested in teams and technology in this area since 2016,” Clegg wrote in the post.

But there are other troubling signs. YouTube announced last June that it would stop taking down content spreading false claims about the 2020 U.S. election or past elections, and Meta quietly made a similar policy change to its political ad rules in 2022. And as past precedent has shown, the platforms tend to have even less cover outside the West, with major blind spots in local languages and context making misinformation and hate speech not only more pervasive but also more dangerous…(More)”.

Forget technology — politicians pose the gravest misinformation threat


Article by Rasmus Nielsen: “This is set to be a big election year, including in India, Mexico, the US, and probably the UK. People will rightly be on their guard for misinformation, but much of the policy discussion on the topic ignores the most important source: members of the political elite.

As a social scientist working on political communication, I have spent years in these debates — which continue to be remarkably disconnected from what we know from research. Academic findings repeatedly underline the actual impact of politics, while policy documents focus persistently on the possible impact of new technologies.

Most recently, Britain’s National Cyber Security Centre (NCSC) has warned of how “AI-created hyper-realistic bots will make the spread of disinformation easier and the manipulation of media for use in deepfake campaigns will likely become more advanced”. This is similar to warnings from many other public authorities, which ignore the misinformation from the most senior levels of domestic politics. In the US, the Washington Post stopped counting after documenting at least 30,573 false or misleading claims made by Donald Trump as president. In the UK, the non-profit FullFact has reported that as many as 50 MPs — including two prime ministers, cabinet ministers and shadow cabinet ministers — failed to correct false, unevidenced or misleading claims in 2022 alone, despite repeated calls to do so.

These are actual problems of misinformation, and the phenomenon is not new. Both George W Bush and Barack Obama’s administrations obfuscated on Afghanistan. Bush’s government and that of his UK counterpart Tony Blair advanced false and misleading claims in the run-up to the Iraq war. Prominent politicians have, over the years, denied the reality of human-induced climate change, proposed quack remedies for Covid-19, and so much more. These are examples of misinformation, and, at their most egregious, of disinformation — defined as spreading false or misleading information for political advantage or profit.

This basic point is strikingly absent from many policy documents — the NCSC report, for example, has nothing to say about domestic politics. It is not alone. Take the US Surgeon General’s 2021 advisory on confronting health misinformation which calls for a “whole-of-society” approach — and yet contains nothing on politicians and curiously omits the many misleading claims made by the sitting president during the pandemic, including touting hydroxychloroquine as a potential treatment…(More)”.

Eat, Click, Judge: The Rise of Cyber Jurors on China’s Food Apps


Article from Ye Zhanhang: “From unwanted ingredients in takeaway meals and negative restaurant reviews to late deliveries and poor product quality, digital marketplaces teem with minor frustrations. 

But because they affect customer satisfaction and business reputations, several Chinese online shopping platforms have come up with a unique solution: Ordinary users can become “cyber jurors” to deliberate and cast decisive votes in resolving disputes between buyers and sellers.

Though introduced in 2020, the concept has surged in popularity among young Chinese in recent months, primarily fueled by viral cases that users eagerly follow, scrutinizing every detail and deliberation online…

To be eligible for the role, a user must meet certain criteria, including having a verified account, maintaining consumption records within the past three months, and successfully navigating five mock cases as part of an entry test. Cyber jurors don’t receive any money for completing cases but may be rewarded with coupons.

Xianyu, an online secondhand shopping platform, has also introduced a “court” system that assembles a jury of 17 volunteer users to adjudicate disputes between buyers and sellers. 

Miao Mingyu, a law professor at the University of Chinese Academy of Social Sciences, told China Youth Daily that this public jury function, with its impartial third-party perspective, has the potential to enhance transaction transparency and the fairness of the platform’s evaluation system.

Despite Chinese law prohibiting platforms from removing user reviews of products, Miao noted that this feature has enabled the platform to effectively address unfair negative reviews without violating legal constraints…(More)”.

Charting the Emerging Geography of AI


Article by Bhaskar Chakravorti, Ajay Bhalla, and Ravi Shankar Chaturvedi: “Given the high stakes of this race, which countries are in the lead? Which are gaining on the leaders? How might this hierarchy shape the future of AI? Identifying AI-leading countries is not straightforward, as data, knowledge, algorithms, and models can, in principle, cross borders. Even the U.S.–China rivalry is complicated by the fact that AI researchers from the two countries cooperate — and more so than researchers from any other pair of countries. Open-source models are out there for everyone to use, with licensing accessible even for cutting-edge models. Nonetheless, AI development benefits from scale economies and, as a result, is geographically clustered as many significant inputs are concentrated and don’t cross borders that easily….

Rapidly accumulating pools of data in digital economies around the world are clearly one of the critical drivers of AI development. In 2019, we introduced the idea of “gross data product” of countries determined by the volume, complexity, and accessibility of data consumed alongside the number of active internet users in the country. For this analysis, we recognized that gross data product is an essential asset for AI development — especially for generative AI, which requires massive, diverse datasets — and updated the 2019 analyses as a foundation, adding drivers that are critical for AI development overall. That essential data layer makes the index introduced here distinct from other indicators of AI “vibrancy” or measures of global investments, innovations, and implementation of AI…(More)”.

New group aims to professionalize AI auditing


Article by Louise Matsakis: “The newly formed International Association of Algorithmic Auditors (IAAA) is hoping to professionalize the sector by creating a code of conduct for AI auditors, training curriculums, and eventually, a certification program.

Over the last few years, lawmakers and researchers have repeatedly proposed the same solution for regulating artificial intelligence: require independent audits. But the industry remains a wild west; there are only a handful of reputable AI auditing firms and no established guardrails for how they should conduct their work.

Yet several jurisdictions have passed laws mandating tech firms to commission independent audits, including New York City. The idea is that AI firms should have to demonstrate their algorithms work as advertised, the same way companies need to prove they haven’t fudged their finances.

Since ChatGPT was released last year, a troubling norm has been established in the AI industry, which is that it’s perfectly acceptable to evaluate your own models in-house.

Leading startups like OpenAI and Anthropic regularly publish research about the AI systems they’re developing, including the potential risks. But they rarely commission independent audits, let alone publish the results, making it difficult for anyone to know what’s really happening under the hood…(More)”..(More)”

The case for adaptive and end-to-end policy management


Article by Pia Andrews: “Why should we reform how we do policy? Simple. Because the gap between policy design and delivery has become the biggest barrier to delivering good public services and policy outcomes and is a challenge most public servants experience daily, directly or indirectly.

This gap wasn’t always the case, with policy design and delivery separated as part of the New Public Management reforms in the ’90s. When you also consider the accelerating rate of change, increasing cadence of emergencies, and the massive speed and scale of new technologies, you could argue that end-to-end policy reform is our most urgent problem to solve.

Policy teams globally have been exploring new design methods like human-centred design, test-driven iteration (agile), and multi-disciplinary teams that get policy end users in the room (eg, NSW Policy Lab). There has also been an increased focus on improving policy evaluation across the world (eg, the Australian Centre for Evaluation). In both cases, I’m delighted to see innovative approaches being normalised across the policy profession, but it has become obvious that improving design and/or evaluation is still far from sufficient to drive better (or more humane) policy outcomes in an ever-changing world. It is not only the current systemic inability to detect and respond to unintended consequences that emerge but the lack of policy agility that perpetuates issues even long after they might be identified.

Below I outline four current challenges for policy management and a couple of potential solutions, as something of a discussion starter

Problem 1) The separation of (and mutual incomprehension between) policy design, delivery and the public

The lack of multi-disciplinary policy design, combined with a set-and-forget approach to policy, combined with delivery teams being left to interpret policy instructions without support, combined with a gap and interpretation inconsistency between policy modelling systems and policy delivery systems, all combined with a lack of feedback loops in improving policy over time, has led to a series of black holes throughout the process. Tweaking the process as it currently stands will not fix the black holes. We need a more holistic model for policy design, delivery and management…(More)”.

How Moral Can A.I. Really Be?


Article by Paul Bloom: “…The problem isn’t just that people do terrible things. It’s that people do terrible things that they consider morally good. In their 2014 book “Virtuous Violence,” the anthropologist Alan Fiske and the psychologist Tage Rai argue that violence is often itself a warped expression of morality. “People are impelled to violence when they feel that to regulate certain social relationships, imposing suffering or death is necessary, natural, legitimate, desirable, condoned, admired, and ethically gratifying,” they write. Their examples include suicide bombings, honor killings, and war. The philosopher Kate Manne, in her book “Down Girl,” makes a similar point about misogynistic violence, arguing that it’s partially rooted in moralistic feelings about women’s “proper” role in society. Are we sure we want A.I.s to be guided by our idea of morality?

Schwitzgebel suspects that A.I. alignment is the wrong paradigm. “What we should want, probably, is not that superintelligent AI align with our mixed-up, messy, and sometimes crappy values but instead that superintelligent AI have ethically good values,” he writes. Perhaps an A.I. could help to teach us new values, rather than absorbing old ones. Stewart, the former graduate student, argued that if researchers treat L.L.M.s as minds and study them psychologically, future A.I. systems could help humans discover moral truths. He imagined some sort of A.I. God—a perfect combination of all the great moral minds, from Buddha to Jesus. A being that’s better than us.

Would humans ever live by values that are supposed to be superior to our own? Perhaps we’ll listen when a super-intelligent agent tells us that we’re wrong about the facts—“this plan will never work; this alternative has a better chance.” But who knows how we’ll respond if one tells us, “You think this plan is right, but it’s actually wrong.” How would you feel if your self-driving car tried to save animals by refusing to take you to a steakhouse? Would a government be happy with a military A.I. that refuses to wage wars it considers unjust? If an A.I. pushed us to prioritize the interests of others over our own, we might ignore it; if it forced us to do something that we consider plainly wrong, we would consider its morality arbitrary and cruel, to the point of being immoral. Perhaps we would accept such perverse demands from God, but we are unlikely to give this sort of deference to our own creations. We want alignment with our own values, then, not because they are the morally best ones, but because they are ours…(More)”

Informing Decisionmakers in Real Time


Article by Robert M. Groves: “In response, the National Science Foundation (NSF) proposed the creation of a complementary group to provide decisionmakers at all levels with the best available evidence from the social sciences to inform pandemic policymaking. In May 2020, with funding from NSF and additional support from the Alfred P. Sloan Foundation and the David and Lucile Packard Foundation, NASEM established the Societal Experts Action Network (SEAN) to connect “decisionmakers grappling with difficult issues to the evidence, trends, and expert guidance that can help them lead their communities and speed their recovery.” We chose to build a network because of the widespread recognition that no one small group of social scientists would have the expertise or the bandwidth to answer all the questions facing decisionmakers. What was needed was a structure that enabled an ongoing feedback loop between researchers and decisionmakers. This structure would foster the integration of evidence, research, and advice in real time, which broke with NASEM’s traditional form of aggregating expert guidance over lengthier periods.

In its first phase, SEAN’s executive committee set about building a network that could both gather and disseminate knowledge. To start, we brought in organizations of decisionmakers—including the National Association of Counties, the National League of Cities, the International City/County Management Association, and the National Conference of State Legislatures—to solicit their questions. Then we added capacity to the network by inviting social and behavioral organizations—like the National Bureau of Economic Research, the National Hazards Center at the University of Colorado Boulder, the Kaiser Family Foundation, the National Opinion Research Center at the University of Chicago, The Policy Lab at Brown University, and Testing for America—to join and respond to questions and disseminate guidance. In this way, SEAN connected teams of experts with evidence and answers to leaders and communities looking for advice…(More)”.