Will Democracies Stand Up to Big Brother?

Curated on Posted on by Stefaan Verhulst

Article by Simon Johnson, Daron Acemoglu and Sylvia Barmack: “Rapid advances in AI and AI-enhanced surveillance tools have created an urgent need for international norms and coordination to set sensible standards. But with oppressive authoritarian regimes unlikely to cooperate, the world’s democracies should start preparing to play economic hardball…Fiction writers have long imagined scenarios in which every human action is monitored by some malign centralized authority. But now, despite their warnings, we find ourselves careening toward a dystopian future worthy of George Orwell’s 1984. The task of assessing how to protect our rights – as consumers, workers, and citizens – has never been more urgent.

One sensible proposal is to limit patents on surveillance technologies to discourage their development and overuse. All else being equal, this could tilt the development of AI-related technologies away from surveillance applications – at least in the United States and other advanced economies, where patent protections matter, and where venture capitalists will be reluctant to back companies lacking strong intellectual-property rights. But even if such sensible measures are adopted, the world will remain divided between countries with effective safeguards on surveillance and those without them. We therefore also need to consider the legitimate basis for trade between these emergent blocs.

AI capabilities have leapt forward over the past 18 months, and the pace of further development is unlikely to slow. The public release of ChatGPT in November 2022 was the generative-AI shot heard round the world. But just as important has been the equally rapid increase in governments and corporations’ surveillance capabilities. Since generative AI excels at pattern matching, it has made facial recognition remarkably accurate (though not without some major flaws). And the same general approach can be used to distinguish between “good” and problematic behavior, based simply on how people move or comport themselves.

Such surveillance technically leads to “higher productivity,” in the sense that it augments an authority’s ability to compel people to do what they are supposed to be doing. For a company, this means performing jobs at what management considers to be the highest productivity level. For a government, it means enforcing the law or otherwise ensuring compliance with those in power.

Unfortunately, a millennium of experience has established that increased productivity does not necessarily lead to improvements in shared prosperity. Today’s AI-powered surveillance allows overbearing managers and authoritarian political leaders to enforce their rules more effectively. But while productivity may increase, most people will not benefit…(More)”

There’s a model for governing AI. Here it is.

Curated on Posted on by Stefaan Verhulst

Article by Jacinda Ardern: “…On March 15, 2019, a terrorist took the lives of 51 members of New Zealand’s Muslim community in Christchurch. The attacker livestreamed his actions for 17 minutes, and the images found their way onto social media feeds all around the planet. Facebook alone blocked or removed 1.5 million copies of the video in the first 24 hours; in that timeframe, YouTube measured one upload per second.

Afterward, New Zealand was faced with a choice: accept that such exploitation of technology was inevitable or resolve to stop it. We chose to take a stand.

We had to move quickly. The world was watching our response and that of social media platforms. Would we regulate in haste? Would the platforms recognize their responsibility to prevent this from happening again?

New Zealand wasn’t the only nation grappling with the connection between violent extremism and technology. We wanted to create a coalition and knew that France had started to work in this space — so I reached out, leader to leader. In my first conversation with President Emmanuel Macron, he agreed there was work to do and said he was keen to join us in crafting a call to action.

We asked industry, civil society and other governments to join us at the table to agree on a set of actions we could all commit to. We could not use existing structures and bureaucracies because they weren’t equipped to deal with this problem.

Within two months of the attack, we launched the Christchurch Call to Action, and today it has more than 120 members, including governments, online service providers and civil society organizations — united by our shared objective to eliminate terrorist and other violent extremist content online and uphold the principle of a free, open and secure internet.

The Christchurch Call is a large-scale collaboration, vastly different from most top-down approaches. Leaders meet annually to confirm priorities and identify areas of focus, allowing the project to act dynamically. And the Call Secretariat — made up of officials from France and New Zealand — convenes working groups and undertakes diplomatic efforts throughout the year. All members are invited to bring their expertise to solve urgent online problems.

While this multi-stakeholder approach isn’t always easy, it has created change. We have bolstered the power of governments and communities to respond to attacks like the one New Zealand experienced. We have created new crisis-response protocols — which enabled companies to stop the 2022 Buffalo attack livestream within two minutes and quickly remove footage from many platforms. Companies and countries have enacted new trust and safety measures to prevent livestreaming of terrorist and other violent extremist content. And we have strengthened the industry-founded Global Internet Forum to Counter Terrorism with dedicated funding, staff and a multi-stakeholder mission.

We’re also taking on some of the more intransigent problems. The Christchurch Call Initiative on Algorithmic Outcomes, a partnership with companies and researchers, was intended to provide better access to the kind of data needed to design online safety measures to prevent radicalization to violence. In practice, it has much wider ramifications, enabling us to reveal more about the ways in which AI and humans interact.

From its start, the Christchurch Call anticipated the emerging challenges of AI and carved out space to address emerging technologies that threaten to foment violent extremism online. The Christchurch Call is actively tackling these AI issues.

Perhaps the most useful thing the Christchurch Call can add to the AI governance debate is the model itself. It is possible to bring companies, government officials, academics and civil society together not only to build consensus but also to make progress. It’s possible to create tools that address the here and now and also position ourselves to face an unknown future. We need this to deal with AI…(More)”.

Revisiting the Behavioral Revolution in Economics 

Curated on Posted on by Stefaan Verhulst

Article by Antara Haldar: “But the impact of the behavioral revolution outside of microeconomics remains modest. Many scholars are still skeptical about incorporating psychological insights into economics, a field that often models itself after the natural sciences, particularly physics. This skepticism has been further compounded by the widely publicized crisis of replication in psychology.

Macroeconomists, who study the aggregate functioning of economies and explore the impact of factors such as output, inflation, exchange rates, and monetary and fiscal policy, have, in particular, largely ignored the behavioral trend. Their indifference seems to reflect the belief that individual idiosyncrasies balance out, and that the quirky departures from rationality identified by behavioral economists must offset each other. A direct implication of this approach is that quantitative analyses predicated on value-maximizing behavior, such as the dynamic stochastic general equilibrium models that dominate policymaking, need not be improved.

The validity of these assumptions, however, remains uncertain. During banking crises such as the Great Recession of 2008 or the ongoing crisis triggered by the recent collapse of Silicon Valley Bank, the reactions of economic actors – particularly financial institutions and investors – appear to be driven by herd mentality and what John Maynard Keynes referred to as “animal spirits.”…

The roots of economics’ resistance to the behavioral sciences run deep. Over the past few decades, the field has acknowledged exceptions to the prevailing neoclassical paradigm, such as Elinor Ostrom’s solutions to the tragedy of the commons and Akerlof, Michael Spence, and Joseph E. Stiglitz’s work on asymmetric information (all four won the Nobel Prize). At the same time, economists have refused to update the discipline’s core assumptions.

This state of affairs can be likened to an imperial government that claims to uphold the rule of law in its colonies. By allowing for a limited release of pressure at the periphery of the paradigm, economists have managed to prevent significant changes that might undermine the entire system. Meanwhile, the core principles of the prevailing economic model remain largely unchanged.

For economics to reflect human behavior, much less influence it, the discipline must actively engage with human psychology. But as the list of acknowledged exceptions to the neoclassical framework grows, each subsequent breakthrough becomes a potentially existential challenge to the field’s established paradigm, undermining the seductive parsimony that has been the source of its power.

By limiting their interventions to nudges, behavioral economists hoped to align themselves with the discipline. But in doing so, they delivered a ratings-conscious “made for TV” version of a revolution. As Gil Scott-Heron famously reminded us, the real thing will not be televised….(More)”.

How AI could take over elections – and undermine democracy

Curated on Posted on by Stefaan Verhulst

Article by Archon Fung and Lawrence Lessig: “Could organizations use artificial intelligence language models such as ChatGPT to induce voters to behave in specific ways?

Sen. Josh Hawley asked OpenAI CEO Sam Altman this question in a May 16, 2023, U.S. Senate hearing on artificial intelligence. Altman replied that he was indeed concerned that some people might use language models to manipulate, persuade and engage in one-on-one interactions with voters.

Altman did not elaborate, but he might have had something like this scenario in mind. Imagine that soon, political technologists develop a machine called Clogger – a political campaign in a black box. Clogger relentlessly pursues just one objective: to maximize the chances that its candidate – the campaign that buys the services of Clogger Inc. – prevails in an election.

While platforms like Facebook, Twitter and YouTube use forms of AI to get users to spend more time on their sites, Clogger’s AI would have a different objective: to change people’s voting behavior.

As a political scientist and a legal scholar who study the intersection of technology and democracy, we believe that something like Clogger could use automation to dramatically increase the scale and potentially the effectiveness of behavior manipulation and microtargeting techniques that political campaigns have used since the early 2000s. Just as advertisers use your browsing and social media history to individually target commercial and political ads now, Clogger would pay attention to you – and hundreds of millions of other voters – individually.

It would offer three advances over the current state-of-the-art algorithmic behavior manipulation. First, its language model would generate messages — texts, social media and email, perhaps including images and videos — tailored to you personally. Whereas advertisers strategically place a relatively small number of ads, language models such as ChatGPT can generate countless unique messages for you personally – and millions for others – over the course of a campaign.

Second, Clogger would use a technique called reinforcement learning to generate a succession of messages that become increasingly more likely to change your vote. Reinforcement learning is a machine-learning, trial-and-error approach in which the computer takes actions and gets feedback about which work better in order to learn how to accomplish an objective. Machines that can play Go, Chess and many video games better than any human have used reinforcement learning.How reinforcement learning works.

Third, over the course of a campaign, Clogger’s messages could evolve in order to take into account your responses to the machine’s prior dispatches and what it has learned about changing others’ minds. Clogger would be able to carry on dynamic “conversations” with you – and millions of other people – over time. Clogger’s messages would be similar to ads that follow you across different websites and social media…(More)”.

How Differential Privacy Will Affect Estimates of Air Pollution Exposure and Disparities in the United States

Curated on Posted on by Stefaan Verhulst

Article by Madalsa Singh: “Census data is crucial to understand energy and environmental justice outcomes such as poor air quality which disproportionately impact people of color in the U.S. With the advent of sophisticated personal datasets and analysis, Census Bureau is considering adding top-down noise (differential privacy) and post-processing 2020 census data to reduce the risk of identification of individual respondents. Using 2010 demonstration census and pollution data, I find that compared to the original census, differentially private (DP) census significantly changes ambient pollution exposure in areas with sparse populations. White Americans have lowest variability, followed by Latinos, Asian, and Black Americans. DP underestimates pollution disparities for SO2 and PM2.5 while overestimates the pollution disparities for PM10…(More)”.

Shallowfakes

Curated on Posted on by Stefaan Verhulst

Essay by James R. Ostrowski: “…This dystopian fantasy, we are told, is what the average social media feed looks like today: a war zone of high-tech disinformation operations, vying for your attention, your support, your compliance. Journalist Joseph Bernstein, in his 2021 Harper’s piece “Bad News,” attributes this perception of social media to “Big Disinfo” — a cartel of think tanks, academic institutions, and prestige media outlets that spend their days spilling barrels of ink into op-eds about foreign powers’ newest disinformation tactics. The technology’s specific impact is always vague, yet somehow devastating. Democracy is dying, shot in the chest by artificial intelligence.

The problem with Big Disinfo isn’t that disinformation campaigns aren’t happening but that claims of mind-warping, AI-enabled propaganda go largely unscrutinized and often amount to mere speculation. There is little systematic public information about the scale at which foreign governments use deepfakes, bot armies, or generative text in influence ops. What little we know is gleaned through irregular investigations or leaked documents. In lieu of data, Big Disinfo squints into the fog, crying “Bigfoot!” at every oak tree.

Any machine learning researcher will admit that there is a critical disconnect between what’s possible in the lab and what’s happening in the field. Take deepfakes. When the technology was first developed, public discourse was saturated with proclamations that it would slacken society’s grip on reality. A 2019 New York Times op-ed, indicative of the general sentiment of this time, was titled “Deepfakes Are Coming. We Can No Longer Believe What We See.” That same week, Politico sounded the alarm in its article “‘Nightmarish’: Lawmakers brace for swarm of 2020 deepfakes.” A Forbes article asked us to imagine a deepfake video of President Trump announcing a nuclear weapons launch against North Korea. These stories, like others in the genre, gloss over questions of practicality…(More)”.

Governing the Unknown

Curated on Posted on by Stefaan Verhulst

Article by Kaushik Basu: “Technology is changing the world faster than policymakers can devise new ways to cope with it. As a result, societies are becoming polarized, inequality is rising, and authoritarian regimes and corporations are doctoring reality and undermining democracy.

For ordinary people, there is ample reason to be “a little bit scared,” as OpenAI CEO Sam Altman recently put it. Major advances in artificial intelligence raise concerns about education, work, warfare, and other risks that could destabilize civilization long before climate change does. To his credit, Altman is urging lawmakers to regulate his industry.

In confronting this challenge, we must keep two concerns in mind. The first is the need for speed. If we take too long, we may find ourselves closing the barn door after the horse has bolted. That is what happened with the 1968 Nuclear Non-Proliferation Treaty: It came 23 years too late. If we had managed to establish some minimal rules after World War II, the NPT’s ultimate goal of nuclear disarmament might have been achievable.

The other concern involves deep uncertainty. This is such a new world that even those working on AI do not know where their inventions will ultimately take us. A law enacted with the best intentions can still backfire. When America’s founders drafted the Second Amendment conferring the “right to keep and bear arms,” they could not have known how firearms technology would change in the future, thereby changing the very meaning of the word “arms.” Nor did they foresee how their descendants would fail to realize this even after seeing the change.

But uncertainty does not justify fatalism. Policymakers can still effectively govern the unknown as long as they keep certain broad considerations in mind. For example, one idea that came up during a recent Senate hearing was to create a licensing system whereby only select corporations would be permitted to work on AI.

This approach comes with some obvious risks of its own. Licensing can often be a step toward cronyism, so we would also need new laws to deter politicians from abusing the system. Moreover, slowing your country’s AI development with additional checks does not mean that others will adopt similar measures. In the worst case, you may find yourself facing adversaries wielding precisely the kind of malevolent tools that you eschewed. That is why AI is best regulated multilaterally, even if that is a tall order in today’s world…(More)”.

Filling Africa’s Data Gap

Curated on Posted on by Stefaan Verhulst

Article by Jendayi Frazer and Peter Blair Henry: “Every few years, the U.S. government launches a new initiative to boost economic growth in Africa. In bold letters and with bolder promises, the White House announces that public-private partnerships hold the key to growth on the continent. It pledges to make these partnerships a cornerstone of its Africa policy, but time and again it fails to deliver.

A decade after U.S. President Barack Obama rolled out Power Africa—his attempt to solve Africa’s energy crisis by mobilizing private capital—half of the continent’s sub-Saharan population remains without access to electricity. In 2018, the Trump administration proclaimed that its Prosper Africa initiative would counter China’s debt-trap diplomacy and “expand African access to business finance.” Five years on, Chad, Ethiopia, Ghana, and Zambia are in financial distress and pleading for debt relief from Beijing and other creditors. Yet the Biden administration is once more touting the potential of public-private investment in Africa, organizing high-profile visits and holding leadership summits to prove that this time, the United States is “all in” on the continent.

There is a reason these efforts have yielded so little: goodwill tours, clever slogans, and a portfolio of G-7 pet projects in Africa do not amount to a sound investment pitch. Potential investors, public and private, need to know which projects in which countries are economically and financially worthwhile. Above all, that requires current and comprehensive data on the expected returns that investment in infrastructure in the developing world can yield. At present, investors lack this information, so they pass. If the United States wants to “build back better” in Africa—to expand access to business finance and encourage countries on the continent to choose sustainable and high-quality foreign investment over predatory lending from China and Russia—it needs to give investors access to better data…(More)”.

How Would You Defend the Planet From Asteroids? 

Curated on Posted on by Stefaan Verhulst

Article by Mahmud Farooque, Jason L. Kessler: “On September 26, 2022, NASA successfully smashed a spacecraft into a tiny asteroid named Dimorphos, altering its orbit. Although it was 6.8 million miles from Earth, the Double Asteroid Redirect Test (DART) was broadcast in real time, turning the impact into a rare pan-planetary moment accessible from smartphones around the world. 

For most people, the DART mission was the first glimmer—outside of the movies—that NASA was seriously exploring how to protect Earth from asteroids. Rightly famous for its technological prowess, NASA is less recognized for its social innovations. But nearly a decade before DART, the agency had launched the Asteroid Grand Challenge. In a pioneering approach to public engagement, the challenge brought citizens together to weigh in on how the taxpayer-funded agency might approach some technical decisions involving asteroids. 

The following account of how citizens came to engage with strategies for planetary defense—and the unexpected conclusions they reached—is based on the experiences of NASA employees, members of the Expert and Citizen Assessment of Science and Technology (ECAST) network, and forum participants…(More)”.

Technological Obsolescence

Curated on Posted on by Stefaan Verhulst

Essay by Jonathan Coopersmith: “In addition to killing over a million Americans, Covid-19 revealed embarrassing failures of local, state, and national public health systems to accurately and effectively collect, transmit, and process information. To some critics and reporters, the visible and easily understood face of those failures was the continued use of fax machines.

In reality, the critics were attacking the symptom, not the problem. Instead of “why were people still using fax machines?,” the better question was “what factors made fax machines more attractive than more capable technologies?” Those answers provide a better window into the complex, evolving world of technological obsolescence, a key component of our modern world—and on a smaller scale, provide a template to decide whether the NAE and other organizations should retain their fax machines.

The marketing dictionary of Monash University Business School defines technological obsolescence as “when a technical product or service is no longer needed or wanted even though it could still be in working order.” Significantly, the source is a business school, which implies strong economic and social factors in decision making about technology.  

Determining technological obsolescence depends not just on creators and promoters of new technologies but also on users, providers, funders, accountants, managers, standards setters—and, most importantly, competing needs and options. In short, it’s complicated.  

Like most aspects of technology, perspectives on obsolescence depend on your position. If existing technology meets your needs, upgrading may not seem worth the resources needed (e.g., for purchase and training). If, on the other hand, your firm or organization depends on income from providing, installing, servicing, training, advising, or otherwise benefiting from a new technology, not upgrading could jeopardize your future, especially in a very competitive market. And if you cannot find the resources to upgrade, you—and your users—may incur both visible and invisible costs…(More)”.