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New Blog Moderation Policy

There has been a lot of toxicity in the comments section of this blog. Recently, we’re having to delete more and more comments. Not just spam and off-topic comments, but also sniping and personal attacks. It’s gotten so bad that I need to do something.

My options are limited because I’m just one person, and this website is free, ad-free, and anonymous. I pay for a part-time moderator out of pocket; he isn’t able to constantly monitor comments. And I’m unwilling to require verified accounts.

So starting now, we will be pre-screening comments and letting through only those that 1) are on topic, 2) contribute to the discussion, and 3) don’t attack or insult anyone. The standard is not going to be “well, I guess this doesn’t technically quite break a rule,” but “is this actually contributing.”

Obviously, this is a subjective standard; sometimes good comments will accidentally get thrown out. And the delayed nature of the screening will result in less conversation and more disjointed comments. Those are costs, and they’re significant ones. But something has to be done, and I would like to try this before turning off all comments.

I am going to disable comments on the weekly squid posts. Topicality is too murky on an open thread, and these posts are especially hard to keep on top of.

Comments will be reviewed and published when possible, usually in the morning and evening. Sometimes it will take longer. Again, the moderator is part time, so please be patient.

I apologize to all those who have just kept commenting reasonably all along. But I’ve received three e-mails in the past couple of months about people who have given up on comments because of the toxicity.

So let’s see if this works. I’ve been able to maintain an anonymous comment section on this blog for almost twenty years. It’s kind of astounding that it’s worked as long as it has. Maybe its time is up.

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Posted on June 19, 2024 at 4:26 PM • View Comments

The Hacking of Culture and the Creation of Socio-Technical Debt

Culture is increasingly mediated through algorithms. These algorithms have splintered the organization of culture, a result of states and tech companies vying for influence over mass audiences. One byproduct of this splintering is a shift from imperfect but broad cultural narratives to a proliferation of niche groups, who are defined by ideology or aesthetics instead of nationality or geography. This change reflects a material shift in the relationship between collective identity and power, and illustrates how states no longer have exclusive domain over either. Today, both power and culture are increasingly corporate.

Blending Stewart Brand and Jean-Jacques Rousseau, McKenzie Wark writes in A Hacker Manifesto that “information wants to be free but is everywhere in chains.”1 Sounding simultaneously harmless and revolutionary, Wark’s assertion as part of her analysis of the role of what she terms “the hacker class” in creating new world orders points to one of the main ideas that became foundational to the reorganization of power in the era of the internet: that “information wants to be free.” This credo, itself a co-option of Brand’s influential original assertion in a conversation with Apple cofounder Steve Wozniak at the 1984 Hackers Conference and later in his 1987 book The Media Lab: Inventing the Future at MIT, became a central ethos for early internet inventors, activists,2 and entrepreneurs. Ultimately, this notion was foundational in the construction of the era we find ourselves in today: an era in which internet companies dominate public and private life. These companies used the supposed desire of information to be free as a pretext for building platforms that allowed people to connect and share content. Over time, this development helped facilitate the definitive power transfer of our time, from states to corporations.

This power transfer was enabled in part by personal data and its potential power to influence people’s behavior—a critical goal in both politics and business. The pioneers of the digital advertising industry claimed that the more data they had about people, the more they could influence their behavior. In this way, they used data as a proxy for influence, and built the business case for mass digital surveillance. The big idea was that data can accurately model, predict, and influence the behavior of everyone—from consumers to voters to criminals. In reality, the relationship between data and influence is fuzzier, since influence is hard to measure or quantify. But the idea of data as a proxy for influence is appealing precisely because data is quantifiable, whereas influence is vague. The business model of Google Ads, Facebook, Experian, and similar companies works because data is cheap to gather, and the effectiveness of the resulting influence is difficult to measure. The credo was “Build the platform, harvest the data…then profit.” By 2006, a major policy paper could ask, “Is Data the New Oil?”3

The digital platforms that have succeeded most in attracting and sustaining mass attention—Facebook, TikTok, Instagram—have become cultural. The design of these platforms dictates the circulation of customs, symbols, stories, values, and norms that bind people together in protocols of shared identity. Culture, as articulated through human systems such as art and media, is a kind of social infrastructure. Put differently, culture is the operating system of society.

Like any well-designed operating system, culture is invisible to most people most of the time. Hidden in plain sight, we make use of it constantly without realizing it. As an operating system, culture forms the base infrastructure layer of societal interaction, facilitating communication, cooperation, and interrelations. Always evolving, culture is elastic: we build on it, remix it, and even break it.

Culture can also be hacked—subverted for specific advantage.4 If culture is like an operating system, then to hack it is to exploit the design of that system to gain unauthorized control and manipulate it towards a specific end. This can be for good or for bad. The morality of the hack depends on the intent and actions of the hacker.

When businesses hack culture to gather data, they are not necessarily destroying or burning down social fabrics and cultural infrastructure. Rather, they reroute the way information and value circulate, for the benefit of their shareholders. This isn’t new. There have been culture hacks before. For example, by lending it covert support, the CIA hacked the abstract expressionism movement to promote the idea that capitalism was friendly to high culture.5 Advertising appropriated the folk-cultural images of Santa Claus and the American cowboy to sell Coca-Cola and Marlboro cigarettes, respectively. In Mexico, after the revolution of 1910, the ruling party hacked muralist works, aiming to construct a unifying national narrative.

Culture hacks under digital capitalism are different. Whereas traditional propaganda goes in one direction—from government to population, or from corporation to customers—the internet-surveillance business works in two directions: extracting data while pushing engaging content. The extracted data is used to determine what content a user would find most engaging, and that engagement is used to extract more data, and so on. The goal is to keep as many users as possible on platforms for as long as possible, in order to sell access to those users to advertisers. Another difference between traditional propaganda and digital platforms is that the former aims to craft messages with broad appeal, while the latter hyper-personalizes content for individual users.

The rise of Chinese-owned TikTok has triggered heated debate in the US about the potential for a foreign-owned platform to influence users by manipulating what they see. Never mind that US corporations have used similar tactics for years. While the political commitments of platform owners are indeed consequential—Chinese-owned companies are in service to the Chinese Communist Party, while US-owned companies are in service to business goals—the far more pressing issue is that both have virtually unchecked surveillance power. They are both reshaping societies by hacking culture to extract data and serve content. Funny memes, shocking news, and aspirational images all function similarly: they provide companies with unprecedented access to societies’ collective dreams and fears.6 By determining who sees what when and where, platform owners influence how societies articulate their understanding of themselves.

Tech companies want us to believe that algorithmically determined content is effectively neutral: that it merely reflects the user’s behavior and tastes back at them. In 2021, Instagram head Adam Mosseri wrote a post on the company’s blog entitled “Shedding More Light on How Instagram Works.” A similar window into TikTok’s functioning was provided by journalist Ben Smith in his article “How TikTok Reads Your Mind.”7 Both pieces boil down to roughly the same idea: “We use complicated math to give you more of what your behavior shows us you really like.”

This has two consequences. First, companies that control what users see in a nontransparent way influence how we perceive the world. They can even shape our personal relationships. Second, by optimizing algorithms for individual attention, a sense of culture as common ground is lost. Rather than binding people through shared narratives, digital platforms fracture common cultural norms into self-reinforcing filter bubbles.8

This fragmentation of shared cultural identity reflects how the data surveillance business is rewriting both the established order of global power, and social contracts between national governments and their citizens. Before the internet, in the era of the modern state, imperfect but broad narratives shaped distinct cultural identities; “Mexican culture” was different from “French culture,” and so on. These narratives were designed to carve away an “us” from “them,” in a way that served government aims. Culture has long been understood to operate within the envelope of nationality, as exemplified by the organization of museum collections according to the nationality of artists, or by the Venice Biennale—the Olympics of the art world, with its national pavilions format.

National culture, however, is about more than museum collections or promoting tourism. It broadly legitimizes state power by emotionally binding citizens to a self-understood identity. This identity helps ensure a continuing supply of military recruits to fight for the preservation of the state. Sociologist James Davison Hunter, who popularized the phrase “culture war,” stresses that culture is used to justify violence to defend these identities.9 We saw an example of this on January 6, 2021, with the storming of the US Capitol. Many of those involved were motivated by a desire to defend a certain idea of cultural identity they believed was under threat.

Military priorities were also entangled with the origins of the tech industry. The US Department of Defense funded ARPANET, the first version of the internet. But the internet wouldn’t have become what it is today without the influence of both West Coast counterculture and small-l libertarianism, which saw the early internet as primarily a space to connect and play. One of the first digital game designers was Bernie De Koven, founder of the Games Preserve Foundation. A noted game theorist, he was inspired by Stewart Brand’s interest in “play-ins” to start a center dedicated to play. Brand had envisioned play-ins as an alternative form of protest against the Vietnam War; they would be their own “soft war” of subversion against the military.10 But the rise of digital surveillance as the business model of nascent tech corporations would hack this anti-establishment spirit, turning instruments of social cohesion and connection into instruments of control.

It’s this counterculture side of tech’s lineage, which advocated for the social value of play, that attuned the tech industry to the utility of culture. We see the commingling of play and military control in Brand’s Whole Earth Catalog, which was a huge influence on early tech culture. Described as “a kind of Bible for counterculture technology,” the Whole Earth Catalog was popular with the first generation of internet engineers, and established crucial “assumptions about the ideal relationships between information, technology, and community.”11 Brand’s 1972 Rolling Stone article “Spacewar: Fantastic Life and Symbolic Death Among the Computer” further emphasized how rudimentary video games were central to the engineering community. These games were wildly popular at leading engineering research centers: Stanford, MIT, ARPA, Xerox, and others. This passion for gaming as an expression of technical skills and a way for hacker communities to bond led to the development of MUD (Multi-User Dungeon) programs, which enabled multiple people to communicate and collaborate online simultaneously.

The first MUD was developed in 1978 by engineers who wanted to play fantasy games online. It applied the early-internet ethos of decentralism and personalization to video games, making it a precursor to massive multiplayer online role-playing games and modern chat rooms and Facebook groups. Today, these video games and game-like simulations—now a commercial industry worth around $200 billion12—serve as important recruitment and training tools for the military.13 The history of the tech industry and culture is full of this tension between the internet as an engineering plaything and as a surveillance commodity.

Historically, infrastructure businesses—like railroad companies in the nineteenth-century US—have always wielded considerable power. Internet companies that are also infrastructure businesses combine commercial interests with influence over national and individual security. As we transitioned from railroad tycoons connecting physical space to cloud computing companies connecting digital space, the pace of technological development put governments at a disadvantage. The result is that corporations now lead the development of new tech (a reversal from the ARPANET days), and governments follow, struggling to modernize public services in line with the new tech. Companies like Microsoft are functionally providing national cybersecurity. Starlink, Elon Musk’s satellite internet service, is a consumer product that facilitates military communications for the war in Ukraine. Traditionally, this kind of service had been restricted to selected users and was the purview of states.14 Increasingly, it is clear that a handful of transnational companies are using their technological advantages to consolidate economic and political power to a degree previously afforded to only great-power nations.

Worse, since these companies operate across multiple countries and regions, there is no regulatory body with the jurisdiction to effectively constrain them. This transition of authority from states to corporations and the nature of surveillance as the business model of the internet rewrites social contracts between national governments and their citizens. But it also also blurs the lines among citizen, consumer, and worker. An example of this are Google’s Recaptchas, visual image puzzles used in cybersecurity to “prove” that the user is a human and not a bot. While these puzzles are used by companies and governments to add a layer of security to their sites, their value is in how they record a user’s input in solving the puzzles to train Google’s computer vision AI systems. Similarly, Microsoft provides significant cybersecurity services to governments while it also trains its AI models on citizens’ conversations with Bing.15 Under this dyanmic, when citizens use digital tools and services provided by tech companies, often to access government webpages and resources, they become de facto free labor for the tech companies providing them. The value generated by this citizen-user-laborer stays with the company, as it is used to develop and refine their products. In this new blurred reality, the relationships among corporations, governments, power, and identity are shifting. Our social and cultural infrastructure suffers as a result, creating a new kind of technical debt of social and cultural infrustructure.

In the field of software development, technical debt refers to the future cost of ignoring a near-term engineering problem.16 Technical debt grows as engineers implement short-term patches or workarounds, choosing to push the more expensive and involved re-engineering fixes for later. This debt accrues over time, to be paid back in the long term. The result of a decision to solve an immediate problem at the expense of the long-term one effectively mortgages the future in favor of an easier present. In terms of cultural and social infrastructure, we use the same phrase to refer to the long-term costs that result from avoiding or not fully addressing social needs in the present. More than a mere mistake, socio-technical debt stems from willfully not addressing a social problem today and leaving a much larger problem to be addressed in the future.

For example, this kind of technical debt was created by the cratering of the news industry, which relied on social media to drive traffic—and revenue—to news websites. When social media companies adjusted their algorithms to deprioritize news, traffic to news sites plummeted, causing an existential crisis for many publications.17 Now, traditional news stories make up only 3 percent of social media content. At the same time, 66 percent of people ages eighteen to twenty-four say they get their “news” from TikTok, Facebook, and Twitter.18 To be clear, Facebook did not accrue technical debt when it swallowed the news industry. We as a society are dealing with technical debt in the sense that we are being forced to pay the social cost of allowing them to do that.

One result of this shift in information consumption as a result of changes to the cultural infrastructure of social media is the rise in polarization and radicalism. So by neglecting to adequately regulate tech companies and support news outlets in the near term, our governments have paved the way for social instability in the long term. We as a society also have to find and fund new systems to act as a watchdog over both corporate and governmental power.

Another example of socio-technical debt is the slow erosion of main streets and malls by e-commerce.19 These places used to be important sites for physical gathering, which helped the shops and restaurants concentrated there stay in business. But e-commerce and direct-to-consumer trends have undermined the economic viability of main streets and malls, and have made it much harder for small businesses to survive. The long-term consequence of this to society is the hollowing out of town centers and the loss of spaces for physical gathering—which we will all have to pay for eventually.

The faltering finances of museums will also create long-term consequences for society as a whole, especially in the US, where Museums mostly depend on private donors to cover operational costs. But a younger generation of philanthropists is shifting its giving priorities away from the arts, leading to a funding crisis at some institutions.20

One final example: libraries. NYU Sociologist Eric Klinenberg called libraries “the textbook example of social infrastructure in action.”21 But today they are stretched to the breaking point, like museums, main streets, and news media. In New York City, Mayor Eric Adams has proposed a series of severe budget cuts to the city’s library system over the past year, despite having seen a spike in usage recently. The steepest cuts were eventually retracted, but most libraries in the city have still had to cancel social programs and cut the number of days they’re open.22 As more and more spaces for meeting in real life close, we increasingly turn to digital platforms for connection to replace them. But these virtual spaces are optimized for shareholder returns, not public good.

Just seven companies—Alphabet (the parent company of Google), Amazon, Apple, Meta, Microsoft, Nvidia and Tesla—drove 60 percent of the gains of the S&P stock market index in 2023.23 Four—Alibaba, Amazon, Google, and Microsoft—deliver the majority of cloud services.24 These companies have captured the delivery of digital and physical goods and services. Everything involved with social media, cloud computing, groceries, and medicine is trapped in their flywheels, because the constellation of systems that previously put the brakes on corporate power, such as monopoly laws, labor unions, and news media, has been eroded. Product dependence and regulatory capture have further undermined the capacity of states to respond to the rise in corporate hard and soft power. Lock-in and other anticompetitive corporate behavior have prevented market mechanisms from working properly. As democracy falls into deeper crisis with each passing year, policy and culture are increasingly bent towards serving corporate interest. The illusion that business, government, and culture are siloed sustains this status quo.

Our digitized global economy has made us all participants in the international data trade, however reluctantly. Though we are aware of the privacy invasions and social costs of digital platforms, we nevertheless participate in these systems because we feel as though we have no alternative—which itself is partly the result of tech monopolies and the lack of competition.

Now, the ascendence of AI is thrusting big data into a new phase and new conflicts with social contracts. The development of bigger, more powerful AI models means more demand for data. Again, massive wholesale extractions of culture are at the heart of these efforts.25 As AI researchers and artists Kate Crawford and Vladan Joler explain in the catalog to their exhibition Calculating Empires, AI developers require “the entire history of human knowledge and culture … The current lawsuits over generative systems like GPT and Stable Diffusion highlight how completely dependent AI systems are on extracting, enclosing, and commodifying the entire history of cognitive and creative labor.”26

Permitting internet companies to hack the systems in which culture is produced and circulates is a short-term trade-off that has proven to have devastating long-term consequences. When governments give tech companies unregulated access to our social and cultural infrastructure, the social contract becomes biased towards their profit. When we get immediate catharsis through sharing memes or engaging in internet flamewars, real protest is muzzled. We are increasing our collective socio-technical debt by ceding our social and cultural infrastructure to tech monopolies.

Cultural expression is fundamental to what makes us human. It’s an impulse, innate to us as a species, and this impulse will continue to be a gold mine to tech companies. There is evidence that AI models trained on synthetic data—data produced by other AI models rather than humans—can corrupt these models, causing them to return false or nonsensical answers to queries.27 So as AI-produced data floods the internet, data that is guaranteed to have been derived from humans becomes more valuable. In this context, our human nature, compelling us to make and express culture, is the dream of digital capitalism. We become a perpetual motion machine churning out free data. Beholden to shareholders, these corporations see it as their fiduciary duty—a moral imperative even—to extract value from this cultural life.

We are in a strange transition. The previous global order, in which states wielded ultimate authority, hasn’t quite died. At the same time, large corporations have stepped in to deliver some of the services abandoned by states, but at the price of privacy and civic well-being. Increasingly, corporations provide consistent, if not pleasant, economic and social organization. Something similar occurred during the Gilded Age in the US (1870s–1890s). But back then, the influence of robber barons was largely constrained to the geographies in which they operated, and their services (like the railroad) were not previously provided by states. In our current transitionary period, public life worldwide is being reimagined in accordance with corporate values. Amidst a tug-of-war between the old state-centric world and the emerging capital-centric world, there is a growing radicalism fueled partly by frustration over social and personal needs going unmet under a transnational order that is maximized for profit rather than public good.

Culture is increasingly divorced from national identity in our globalized, fragmented world. On the positive side, this decoupling can make culture more inclusive of marginalized people. Other groups, however, may perceive this new status quo as a threat, especially those facing a loss of privilege. The rise of white Christian nationalism shows that the right still regards national identity and culture as crucial—as potent tools in the struggle to build political power, often through anti-democratic means. This phenomenon shows that the separation of cultural identity from national identity doesn’t negate the latter. Instead, it creates new political realities and new orders of power.

Nations issuing passports still behave as though they are the definitive arbiters of identity. But culture today—particularly the multiverse of internet cultures—exposes how this is increasingly untrue. With government discredited as an ultimate authority, and identity less and less connected to nationality, we can find a measure of hope for navigating the current transition in the fact that culture is never static. New forms of resistance are always emerging. But we must ask ourselves: Have the tech industry’s overwhelming surveillance powers rendered subversion impossible? Or does its scramble to gather all the world’s data offer new possibilities to hack the system?

 

1. McKenzie Wark, A Hacker Manifesto (Harvard University Press, 2004), thesis 126. ↑

2. Jon Katz, “Birth of a Digital Nation,” Wired, April 1, 1997. ↑

3. Marcin Szczepanski, “Is Data the New Oil? Competition Issues in the Digital Economy,” European Parliamentary Research Service, January 2020. ↑

4. Bruce Schneier, A Hacker’s Mind: How the Powerful Bend Society’s Rules, and How to Bend Them Back (W. W. Norton & Sons, 2023). ↑

5. Lucie Levine, “Was Modern Art Really a CIA Psy-Op?” JStor Daily, April 1, 2020. ↑

6. Bruce Schneier, Data and Goliath: The Hidden Battles to Collect Your Data and Control Your World (W. W. Norton & Sons, 2015). ↑

7. Adam Mosseri, “Shedding More Light on How Instagram Works,” Instagram Blog, June 8, 2021; Ben Smith, “How TikTok Reads Your Mind,” New York Times, December 5, 2021. ↑

8. Giacomo Figà Talamanca and Selene Arfini, “Through the Newsfeed Glass: Rethinking Filter Bubbles and Echo Chambers,” Philosophy & Technology 35, no. 1 (2022). ↑

9. Zack Stanton, “How the ‘Culture War’ Could Break Democracy,” Politico, May 5, 2021. ↑

10. Jason Johnson, “Inside the Failed, Utopian New Games Movement,” Kill Screen, October 25, 2013. ↑

11. Fred Turner, “Taking the Whole Earth Digital,” chap. 4 in From Counter Culture to Cyberculture: Stewart Brand, The Whole Earth Network, and the Rise of Digital Utopianism (University of Chicago Press, 2006). ↑

12. Kaare Ericksen, “The State of the Video Games Industry: A Special Report,” Variety, February 1, 2024. ↑

13. Rosa Schwartzburg, “The US Military Is Embedded in the Gaming World. It’s Target: Teen Recruits,” The Guardian, February 14, 2024; Scott Kuhn, “Soldiers Maintain Readiness Playing Video Games,” US Army, April 29, 2020; Katie Lange, “Military Esports: How Gaming Is Changing Recruitment & Moral,” US Department of Defense, December 13, 2022. ↑

14. Shaun Waterman, “Growing Commercial SATCOM Raises Trust Issues for Pentagon,” Air & Space Forces Magazine, April 3, 2024. ↑

15. Geoffrey A Fowler, “Your Instagrams Are Training AI. There’s Little You Can Do About It,” Washington Post, September 27, 2023. ↑

16. Zengyang Li, Paris Avgeriou, and Peng Liang, “A Systematic Mapping Study on Technical Debt and Its Management,” Journal of Systems and Software, December 2014. ↑

17. David Streitfeld, “How the Media Industry Keeps Losing the Future,” New York Times, February 28, 2024. ↑

18. “The End of the Social Network,” The Economist, February 1, 2024; Ollie Davies, “What Happens If Teens Get Their News From TikTok?” The Guardian, February 22, 2023. ↑

19. Eric Jaffe, “Quantifying the Death of the Classic American Main Street,” Medium, March 16, 2018. ↑

20. Julia Halprin, “The Hangover from the Museum Party: Institutions in the US Are Facing a Funding Crisis,” Art Newspaper, January 19, 2024. ↑

21. Quoted in Pete Buttigieg, “The Key to Happiness Might Be as Simple as a Library or Park,” New York Times, September 14, 2018. ↑

22. Jeffery C. Mays and Dana Rubinstein, “Mayor Adams Walks Back Budget Cuts Many Saw as Unnecessary,” New York Times, April 24, 2024. ↑

23. Karl Russell and Joe Rennison, “These Seven Tech Stocks Are Driving the Market,” New York Times, January 22, 2024. ↑

24. Ian Bremmer, “How Big Tech Will Reshape the Global Order,” Foreign Affairs, October 19, 2021. ↑

25. Nathan Sanders and Bruce Schneier, “How the ‘Frontier’ Became the Slogan for Uncontrolled AI,” Jacobin, February 27, 2024. ↑

26. Kate Crawford and Vladan Joler, Calculating Empires: A Genealogy of Technology and Power, 1500–2025 (Fondazione Prada, 2023), 9. Exhibition catalog. ↑

27. Rahul Rao, “AI Generated Data Can Poison Future AI Models,” Scientific American, July 28, 2023. ↑

This essay was written with Kim Córdova, and was originally published in e-flux.

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Posted on June 19, 2024 at 7:09 AM • View Comments

Rethinking Democracy for the Age of AI

There is a lot written about technology’s threats to democracy. Polarization. Artificial intelligence. The concentration of wealth and power. I have a more general story: The political and economic systems of governance that were created in the mid-18th century are poorly suited for the 21st century. They don’t align incentives well. And they are being hacked too effectively.

At the same time, the cost of these hacked systems has never been greater, across all human history. We have become too powerful as a species. And our systems cannot keep up with fast-changing disruptive technologies.

We need to create new systems of governance that align incentives and are resilient against hacking … at every scale. From the individual all the way up to the whole of society.

For this, I need you to drop your 20th century either/or thinking. This is not about capitalism versus communism. It’s not about democracy versus autocracy. It’s not even about humans versus AI. It’s something new, something we don’t have a name for yet. And it’s “blue sky” thinking, not even remotely considering what’s feasible today.

Throughout this talk, I want you to think of both democracy and capitalism as information systems. Socio-technical information systems. Protocols for making group decisions. Ones where different players have different incentives. These systems are vulnerable to hacking and need to be secured against those hacks.

We security technologists have a lot of expertise in both secure system design and hacking. That’s why we have something to add to this discussion.

And finally, this is a work in progress. I’m trying to create a framework for viewing governance. So think of this more as a foundation for discussion, rather than a road map to a solution. And I think by writing, and what you’re going to hear is the current draft of my writing—and my thinking. So everything is subject to change without notice.

OK, so let’s go.

We all know about misinformation and how it affects democracy. And how propagandists have used it to advance their agendas. This is an ancient problem, amplified by information technologies. Social media platforms that prioritize engagement. “Filter bubble” segmentation. And technologies for honing persuasive messages.

The problem ultimately stems from the way democracies use information to make policy decisions. Democracy is an information system that leverages collective intelligence to solve political problems. And then to collect feedback as to how well those solutions are working. This is different from autocracies that don’t leverage collective intelligence for political decision making. Or have reliable mechanisms for collecting feedback from their populations.

Those systems of democracy work well, but have no guardrails when fringe ideas become weaponized. That’s what misinformation targets. The historical solution for this was supposed to be representation. This is currently failing in the US, partly because of gerrymandering, safe seats, only two parties, money in politics and our primary system. But the problem is more general.

James Madison wrote about this in 1787, where he made two points. One, that representatives serve to filter popular opinions, limiting extremism. And two, that geographical dispersal makes it hard for those with extreme views to participate. It’s hard to organize. To be fair, these limitations are both good and bad. In any case, current technology—social media—breaks them both.

So this is a question: What does representation look like in a world without either filtering or geographical dispersal? Or, how do we avoid polluting 21st century democracy with prejudice, misinformation and bias. Things that impair both the problem solving and feedback mechanisms.

That’s the real issue. It’s not about misinformation, it’s about the incentive structure that makes misinformation a viable strategy.

This is problem No. 1: Our systems have misaligned incentives. What’s best for the small group often doesn’t match what’s best for the whole. And this is true across all sorts of individuals and group sizes.

Now, historically, we have used misalignment to our advantage. Our current systems of governance leverage conflict to make decisions. The basic idea is that coordination is inefficient and expensive. Individual self-interest leads to local optimizations, which results in optimal group decisions.

But this is also inefficient and expensive. The U.S. spent $14.5 billion on the 2020 presidential, senate and congressional elections. I don’t even know how to calculate the cost in attention. That sounds like a lot of money, but step back and think about how the system works. The economic value of winning those elections are so great because that’s how you impose your own incentive structure on the whole.

More generally, the cost of our market economy is enormous. For example, $780 billion is spent world-wide annually on advertising. Many more billions are wasted on ventures that fail. And that’s just a fraction of the total resources lost in a competitive market environment. And there are other collateral damages, which are spread non-uniformly across people.

We have accepted these costs of capitalism—and democracy—because the inefficiency of central planning was considered to be worse. That might not be true anymore. The costs of conflict have increased. And the costs of coordination have decreased. Corporations demonstrate that large centrally planned economic units can compete in today’s society. Think of Walmart or Amazon. If you compare GDP to market cap, Apple would be the eighth largest country on the planet. Microsoft would be the tenth.

Another effect of these conflict-based systems is that they foster a scarcity mindset. And we have taken this to an extreme. We now think in terms of zero-sum politics. My party wins, your party loses. And winning next time can be more important than governing this time. We think in terms of zero-sum economics. My product’s success depends on my competitors’ failures. We think zero-sum internationally. Arms races and trade wars.

Finally, conflict as a problem-solving tool might not give us good enough answers anymore. The underlying assumption is that if everyone pursues their own self interest, the result will approach everyone’s best interest. That only works for simple problems and requires systemic oppression. We have lots of problems—complex, wicked, global problems—that don’t work that way. We have interacting groups of problems that don’t work that way. We have problems that require more efficient ways of finding optimal solutions.

Note that there are multiple effects of these conflict-based systems. We have bad actors deliberately breaking the rules. And we have selfish actors taking advantage of insufficient rules.

The latter is problem No. 2: What I refer to as “hacking” in my latest book: “A Hacker’s Mind.” Democracy is a socio-technical system. And all socio-technical systems can be hacked. By this I mean that the rules are either incomplete or inconsistent or outdated—they have loopholes. And these can be used to subvert the rules. This is Peter Thiel subverting the Roth IRA to avoid paying taxes on $5 billion in income. This is gerrymandering, the filibuster, and must-pass legislation. Or tax loopholes, financial loopholes, regulatory loopholes.

In today’s society, the rich and powerful are just too good at hacking. And it is becoming increasingly impossible to patch our hacked systems. Because the rich use their power to ensure that the vulnerabilities don’t get patched.

This is bad for society, but it’s basically the optimal strategy in our competitive governance systems. Their zero-sum nature makes hacking an effective, if parasitic, strategy. Hacking isn’t a new problem, but today hacking scales better—and is overwhelming the security systems in place to keep hacking in check. Think about gun regulations, climate change, opioids. And complex systems make this worse. These are all non-linear, tightly coupled, unrepeatable, path-dependent, adaptive, co-evolving systems.

Now, add into this mix the risks that arise from new and dangerous technologies such as the internet or AI or synthetic biology. Or molecular nanotechnology, or nuclear weapons. Here, misaligned incentives and hacking can have catastrophic consequences for society.

This is problem No. 3: Our systems of governance are not suited to our power level. They tend to be rights based, not permissions based. They’re designed to be reactive, because traditionally there was only so much damage a single person could do.

We do have systems for regulating dangerous technologies. Consider automobiles. They are regulated in many ways: drivers licenses + traffic laws + automobile regulations + road design. Compare this to aircrafts. Much more onerous licensing requirements, rules about flights, regulations on aircraft design and testing and a government agency overseeing it all day-to-day. Or pharmaceuticals, which have very complex rules surrounding everything around researching, developing, producing and dispensing. We have all these regulations because this stuff can kill you.

The general term for this kind of thing is the “precautionary principle.” When random new things can be deadly, we prohibit them unless they are specifically allowed.

So what happens when a significant percentage of our jobs are as potentially damaging as a pilot’s? Or even more damaging? When one person can affect everyone through synthetic biology. Or where a corporate decision can directly affect climate. Or something in AI or robotics. Things like the precautionary principle are no longer sufficient. Because breaking the rules can have global effects.

And AI will supercharge hacking. We have created a series of non-interoperable systems that actually interact and AI will be able to figure out how to take advantage of more of those interactions: finding new tax loopholes or finding new ways to evade financial regulations. Creating “micro-legislation” that surreptitiously benefits a particular person or group. And catastrophic risk means this is no longer tenable.

So these are our core problems: misaligned incentives leading to too effective hacking of systems where the costs of getting it wrong can be catastrophic.

Or, to put more words on it: Misaligned incentives encourage local optimization, and that’s not a good proxy for societal optimization. This encourages hacking, which now generates greater harm than at any point in the past because the amount of damage that can result from local optimization is greater than at any point in the past.

OK, let’s get back to the notion of democracy as an information system. It’s not just democracy: Any form of governance is an information system. It’s a process that turns individual beliefs and preferences into group policy decisions. And, it uses feedback mechanisms to determine how well those decisions are working and then makes corrections accordingly.

Historically, there are many ways to do this. We can have a system where no one’s preference matters except the monarch’s or the nobles’ or the landowners’. Sometimes the stronger army gets to decide—or the people with the money.

Or we could tally up everyone’s preferences and do the thing that at least half of the people want. That’s basically the promise of democracy today, at its ideal. Parliamentary systems are better, but only in the margins—and it all feels kind of primitive. Lots of people write about how informationally poor elections are at aggregating individual preferences. It also results in all these misaligned incentives.

I realize that democracy serves different functions. Peaceful transition of power, minimizing harm, equality, fair decision making, better outcomes. I am taking for granted that democracy is good for all those things. I’m focusing on how we implement it.

Modern democracy uses elections to determine who represents citizens in the decision-making process. And all sorts of other ways to collect information about what people think and want, and how well policies are working. These are opinion polls, public comments to rule-making, advocating, lobbying, protesting and so on. And, in reality, it’s been hacked so badly that it does a terrible job of executing on the will of the people, creating further incentives to hack these systems.

To be fair, the democratic republic was the best form of government that mid 18th century technology could invent. Because communications and travel were hard, we needed to choose one of us to go all the way over there and pass laws in our name. It was always a coarse approximation of what we wanted. And our principles, values, conceptions of fairness; our ideas about legitimacy and authority have evolved a lot since the mid 18th century. Even the notion of optimal group outcomes depended on who was considered in the group and who was out.

But democracy is not a static system, it’s an aspirational direction. One that really requires constant improvement. And our democratic systems have not evolved at the same pace that our technologies have. Blocking progress in democracy is itself a hack of democracy.

Today we have much better technology that we can use in the service of democracy. Surely there are better ways to turn individual preferences into group policies. Now that communications and travel are easy. Maybe we should assign representation by age, or profession or randomly by birthday. Maybe we can invent an AI that calculates optimal policy outcomes based on everyone’s preferences.

Whatever we do, we need systems that better align individual and group incentives, at all scales. Systems designed to be resistant to hacking. And resilient to catastrophic risks. Systems that leverage cooperation more and conflict less. And are not zero-sum.

Why can’t we have a game where everybody wins?

This has never been done before. It’s not capitalism, it’s not communism, it’s not socialism. It’s not current democracies or autocracies. It would be unlike anything we’ve ever seen.

Some of this comes down to how trust and cooperation work. When I wrote “Liars and Outliers” in 2012, I wrote about four systems for enabling trust: our innate morals, concern about our reputations, the laws we live under and security technologies that constrain our behavior. I wrote about how the first two are more informal than the last two. And how the last two scale better, and allow for larger and more complex societies. They enable cooperation amongst strangers.

What I didn’t appreciate is how different the first and last two are. Morals and reputation are both old biological systems of trust. They’re person to person, based on human connection and cooperation. Laws—and especially security technologies—are newer systems of trust that force us to cooperate. They’re socio-technical systems. They’re more about confidence and control than they are about trust. And that allows them to scale better. Taxi driver used to be one of the country’s most dangerous professions. Uber changed that through pervasive surveillance. My Uber driver and I don’t know or trust each other, but the technology lets us both be confident that neither of us will cheat or attack each other. Both drivers and passengers compete for star rankings, which align local and global incentives.

In today’s tech-mediated world, we are replacing the rituals and behaviors of cooperation with security mechanisms that enforce compliance. And innate trust in people with compelled trust in processes and institutions. That scales better, but we lose the human connection. It’s also expensive, and becoming even more so as our power grows. We need more security for these systems. And the results are much easier to hack.

But here’s the thing: Our informal human systems of trust are inherently unscalable. So maybe we have to rethink scale.

Our 18th century systems of democracy were the only things that scaled with the technology of the time. Imagine a group of friends deciding where to have dinner. One is kosher, one is a vegetarian. They would never use a winner-take-all ballot to decide where to eat. But that’s a system that scales to large groups of strangers.

Scale matters more broadly in governance as well. We have global systems of political and economic competition. On the other end of the scale, the most common form of governance on the planet is socialism. It’s how families function: people work according to their abilities, and resources are distributed according to their needs.

I think we need governance that is both very large and very small. Our catastrophic technological risks are planetary-scale: climate change, AI, internet, bio-tech. And we have all the local problems inherent in human societies. We have very few problems anymore that are the size of France or Virginia. Some systems of governance work well on a local level but don’t scale to larger groups. But now that we have more technology, we can make other systems of democracy scale.

This runs headlong into historical norms about sovereignty. But that’s already becoming increasingly irrelevant. The modern concept of a nation arose around the same time as the modern concept of democracy. But constituent boundaries are now larger and more fluid, and depend a lot on context. It makes no sense that the decisions about the “drug war”—or climate migration—are delineated by nation. The issues are much larger than that. Right now there is no governance body with the right footprint to regulate Internet platforms like Facebook. Which has more users world-wide than Christianity.

We also need to rethink growth. Growth only equates to progress when the resources necessary to grow are cheap and abundant. Growth is often extractive. And at the expense of something else. Growth is how we fuel our zero-sum systems. If the pie gets bigger, it’s OK that we waste some of the pie in order for it to grow. That doesn’t make sense when resources are scarce and expensive. Growing the pie can end up costing more than the increase in pie size. Sustainability makes more sense. And a metric more suited to the environment we’re in right now.

Finally, agility is also important. Back to systems theory, governance is an attempt to control complex systems with complicated systems. This gets harder as the systems get larger and more complex. And as catastrophic risk raises the costs of getting it wrong.

In recent decades, we have replaced the richness of human interaction with economic models. Models that turn everything into markets. Market fundamentalism scaled better, but the social cost was enormous. A lot of how we think and act isn’t captured by those models. And those complex models turn out to be very hackable. Increasingly so at larger scales.

Lots of people have written about the speed of technology versus the speed of policy. To relate it to this talk: Our human systems of governance need to be compatible with the technologies they’re supposed to govern. If they’re not, eventually the technological systems will replace the governance systems. Think of Twitter as the de facto arbiter of free speech.

This means that governance needs to be agile. And able to quickly react to changing circumstances. Imagine a court saying to Peter Thiel: “Sorry. That’s not how Roth IRAs are supposed to work. Now give us our tax on that $5B.” This is also essential in a technological world: one that is moving at unprecedented speeds, where getting it wrong can be catastrophic and one that is resource constrained. Agile patching is how we maintain security in the face of constant hacking—and also red teaming. In this context, both journalism and civil society are important checks on government.

I want to quickly mention two ideas for democracy, one old and one new. I’m not advocating for either. I’m just trying to open you up to new possibilities. The first is sortition. These are citizen assemblies brought together to study an issue and reach a policy decision. They were popular in ancient Greece and Renaissance Italy, and are increasingly being used today in Europe. The only vestige of this in the U.S. is the jury. But you can also think of trustees of an organization. The second idea is liquid democracy. This is a system where everybody has a proxy that they can transfer to someone else to vote on their behalf. Representatives hold those proxies, and their vote strength is proportional to the number of proxies they have. We have something like this in corporate proxy governance.

Both of these are algorithms for converting individual beliefs and preferences into policy decisions. Both of these are made easier through 21st century technologies. They are both democracies, but in new and different ways. And while they’re not immune to hacking, we can design them from the beginning with security in mind.

This points to technology as a key component of any solution. We know how to use technology to build systems of trust. Both the informal biological kind and the formal compliance kind. We know how to use technology to help align incentives, and to defend against hacking.

We talked about AI hacking; AI can also be used to defend against hacking, finding vulnerabilities in computer code, finding tax loopholes before they become law and uncovering attempts at surreptitious micro-legislation.

Think back to democracy as an information system. Can AI techniques be used to uncover our political preferences and turn them into policy outcomes, get feedback and then iterate? This would be more accurate than polling. And maybe even elections. Can an AI act as our representative? Could it do a better job than a human at voting the preferences of its constituents?

Can we have an AI in our pocket that votes on our behalf, thousands of times a day, based on the preferences it infers we have. Or maybe based on the preferences it infers we would have if we read up on the issues and weren’t swayed by misinformation. It’s just another algorithm for converting individual preferences into policy decisions. And it certainly solves the problem of people not paying attention to politics.

But slow down: This is rapidly devolving into technological solutionism. And we know that doesn’t work.

A general question to ask here is when do we allow algorithms to make decisions for us? Sometimes it’s easy. I’m happy to let my thermostat automatically turn my heat on and off or to let an AI drive a car or optimize the traffic lights in a city. I’m less sure about an AI that sets tax rates, or corporate regulations or foreign policy. Or an AI that tells us that it can’t explain why, but strongly urges us to declare war—right now. Each of these is harder because they are more complex systems: non-local, multi-agent, long-duration and so on. I also want any AI that works on my behalf to be under my control. And not controlled by a large corporate monopoly that allows me to use it.

And learned helplessness is an important consideration. We’re probably OK with no longer needing to know how to drive a car. But we don’t want a system that results in us forgetting how to run a democracy. Outcomes matter here, but so do mechanisms. Any AI system should engage individuals in the process of democracy, not replace them.

So while an AI that does all the hard work of governance might generate better policy outcomes. There is social value in a human-centric political system, even if it is less efficient. And more technologically efficient preference collection might not be better, even if it is more accurate.

Procedure and substance need to work together. There is a role for AI in decision making: moderating discussions, highlighting agreements and disagreements helping people reach consensus. But it is an independent good that we humans remain engaged in—and in charge of—the process of governance.

And that value is critical to making democracy function. Democratic knowledge isn’t something that’s out there to be gathered: It’s dynamic; it gets produced through the social processes of democracy. The term of art is “preference formation.” We’re not just passively aggregating preferences, we create them through learning, deliberation, negotiation and adaptation. Some of these processes are cooperative and some of these are competitive. Both are important. And both are needed to fuel the information system that is democracy.

We’re never going to remove conflict and competition from our political and economic systems. Human disagreement isn’t just a surface feature; it goes all the way down. We have fundamentally different aspirations. We want different ways of life. I talked about optimal policies. Even that notion is contested: optimal for whom, with respect to what, over what time frame? Disagreement is fundamental to democracy. We reach different policy conclusions based on the same information. And it’s the process of making all of this work that makes democracy possible.

So we actually can’t have a game where everybody wins. Our goal has to be to accommodate plurality, to harness conflict and disagreement, and not to eliminate it. While, at the same time, moving from a player-versus-player game to a player-versus-environment game.

There’s a lot missing from this talk. Like what these new political and economic governance systems should look like. Democracy and capitalism are intertwined in complex ways, and I don’t think we can recreate one without also recreating the other. My comments about agility lead to questions about authority and how that interplays with everything else. And how agility can be hacked as well. We haven’t even talked about tribalism in its many forms. In order for democracy to function, people need to care about the welfare of strangers who are not like them. We haven’t talked about rights or responsibilities. What is off limits to democracy is a huge discussion. And Butterin’s trilemma also matters here: that you can’t simultaneously build systems that are secure, distributed, and scalable.

I also haven’t given a moment’s thought to how to get from here to there. Everything I’ve talked about—incentives, hacking, power, complexity—also applies to any transition systems. But I think we need to have unconstrained discussions about what we’re aiming for. If for no other reason than to question our assumptions. And to imagine the possibilities. And while a lot of the AI parts are still science fiction, they’re not far-off science fiction.

I know we can’t clear the board and build a new governance structure from scratch. But maybe we can come up with ideas that we can bring back to reality.

To summarize, the systems of governance we designed at the start of the Industrial Age are ill-suited to the Information Age. Their incentive structures are all wrong. They’re insecure and they’re wasteful. They don’t generate optimal outcomes. At the same time we’re facing catastrophic risks to society due to powerful technologies. And a vastly constrained resource environment. We need to rethink our systems of governance; more cooperation and less competition and at scales that are suited to today’s problems and today’s technologies. With security and precautions built in. What comes after democracy might very well be more democracy, but it will look very different.

This feels like a challenge worthy of our security expertise.

This text is the transcript from a keynote speech delivered during the RSA Conference in San Francisco on April 25, 2023. It was previously published in Cyberscoop. I thought I posted it to my blog and Crypto-Gram last year, but it seems that I didn’t.

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Posted on June 18, 2024 at 7:04 AM • View Comments

Using LLMs to Exploit Vulnerabilities

Interesting research: “Teams of LLM Agents can Exploit Zero-Day Vulnerabilities.”

Abstract: LLM agents have become increasingly sophisticated, especially in the realm of cybersecurity. Researchers have shown that LLM agents can exploit real-world vulnerabilities when given a description of the vulnerability and toy capture-the-flag problems. However, these agents still perform poorly on real-world vulnerabilities that are unknown to the agent ahead of time (zero-day vulnerabilities).

In this work, we show that teams of LLM agents can exploit real-world, zero-day vulnerabilities. Prior agents struggle with exploring many different vulnerabilities and long-range planning when used alone. To resolve this, we introduce HPTSA, a system of agents with a planning agent that can launch subagents. The planning agent explores the system and determines which subagents to call, resolving long-term planning issues when trying different vulnerabilities. We construct a benchmark of 15 real-world vulnerabilities and show that our team of agents improve over prior work by up to 4.5×.

The LLMs aren’t finding new vulnerabilities. They’re exploiting zero-days—which means they are not trained on them—in new ways. So think about this sort of thing combined with another AI that finds new vulnerabilities in code.

These kinds of developments are important to follow, as they are part of the puzzle of a fully autonomous AI cyberattack agent. I talk about this sort of thing more here.

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Posted on June 17, 2024 at 7:08 AM • View Comments

Upcoming Speaking Engagements

This is a current list of where and when I am scheduled to speak:

  • I’m appearing on a panel on Society and Democracy at ACM Collective Intelligence in Boston, Massachusetts. The conference runs from June 26 through 29, 2024, and my panel is at 9:00 AM on Friday, June 28.
  • I’m speaking on “Reimagining Democracy in the Age of AI” at the Bozeman Library in Bozeman, Montana, USA, July 18, 2024. The event will also be available via Zoom.
  • I’m speaking at the TEDxBillings Democracy Event in Billings, Montana, USA, on July 19, 2024.

The list is maintained on this page.

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Posted on June 14, 2024 at 11:59 AM • View Comments

AI and the Indian Election

As India concluded the world’s largest election on June 5, 2024, with over 640 million votes counted, observers could assess how the various parties and factions used artificial intelligence technologies—and what lessons that holds for the rest of the world.

The campaigns made extensive use of AI, including deepfake impersonations of candidates, celebrities and dead politicians. By some estimates, millions of Indian voters viewed deepfakes.

But, despite fears of widespread disinformation, for the most part the campaigns, candidates and activists used AI constructively in the election. They used AI for typical political activities, including mudslinging, but primarily to better connect with voters.

Deepfakes without the deception

Political parties in India spent an estimated US$50 million on authorized AI-generated content for targeted communication with their constituencies this election cycle. And it was largely successful.

Indian political strategists have long recognized the influence of personality and emotion on their constituents, and they started using AI to bolster their messaging. Young and upcoming AI companies like The Indian Deepfaker, which started out serving the entertainment industry, quickly responded to this growing demand for AI-generated campaign material.

In January, Muthuvel Karunanidhi, former chief minister of the southern state of Tamil Nadu for two decades, appeared via video at his party’s youth wing conference. He wore his signature yellow scarf, white shirt, dark glasses and had his familiar stance—head slightly bent sideways. But Karunanidhi died in 2018. His party authorized the deepfake.

In February, the All-India Anna Dravidian Progressive Federation party’s official X account posted an audio clip of Jayaram Jayalalithaa, the iconic superstar of Tamil politics colloquially called “Amma” or “Mother.” Jayalalithaa died in 2016.

Meanwhile, voters received calls from their local representatives to discuss local issues—except the leader on the other end of the phone was an AI impersonation. Bhartiya Janta Party (BJP) workers like Shakti Singh Rathore have been frequenting AI startups to send personalized videos to specific voters about the government benefits they received and asking for their vote over WhatsApp.

Multilingual boost

Deepfakes were not the only manifestation of AI in the Indian elections. Long before the election began, Indian Prime Minister Narendra Modi addressed a tightly packed crowd celebrating links between the state of Tamil Nadu in the south of India and the city of Varanasi in the northern state of Uttar Pradesh. Instructing his audience to put on earphones, Modi proudly announced the launch of his “new AI technology” as his Hindi speech was translated to Tamil in real time.

In a country with 22 official languages and almost 780 unofficial recorded languages, the BJP adopted AI tools to make Modi’s personality accessible to voters in regions where Hindi is not easily understood. Since 2022, Modi and his BJP have been using the AI-powered tool Bhashini, embedded in the NaMo mobile app, to translate Modi’s speeches with voiceovers in Telugu, Tamil, Malayalam, Kannada, Odia, Bengali, Marathi and Punjabi.

As part of their demos, some AI companies circulated their own viral versions of Modi’s famous monthly radio show “Mann Ki Baat,” which loosely translates to “From the Heart,” which they voice cloned to regional languages.

Adversarial uses

Indian political parties doubled down on online trolling, using AI to augment their ongoing meme wars. Early in the election season, the Indian National Congress released a short clip to its 6 million followers on Instagram, taking the title track from a new Hindi music album named “Chor” (thief). The video grafted Modi’s digital likeness onto the lead singer and cloned his voice with reworked lyrics critiquing his close ties to Indian business tycoons.

The BJP retaliated with its own video, on its 7-million-follower Instagram account, featuring a supercut of Modi campaigning on the streets, mixed with clips of his supporters but set to unique music. It was an old patriotic Hindi song sung by famous singer Mahendra Kapoor, who passed away in 2008 but was resurrected with AI voice cloning.

Modi himself quote-tweeted an AI-created video of him dancing—a common meme that alters footage of rapper Lil Yachty on stage—commenting “such creativity in peak poll season is truly a delight.”

In some cases, the violent rhetoric in Modi’s campaign that put Muslims at risk and incited violence was conveyed using generative AI tools, but the harm can be traced back to the hateful rhetoric itself and not necessarily the AI tools used to spread it.

The Indian experience

India is an early adopter, and the country’s experiments with AI serve as an illustration of what the rest of the world can expect in future elections. The technology’s ability to produce nonconsensual deepfakes of anyone can make it harder to tell truth from fiction, but its consensual uses are likely to make democracy more accessible.

The Indian election’s embrace of AI that began with entertainment, political meme wars, emotional appeals to people, resurrected politicians and persuasion through personalized phone calls to voters has opened a pathway for the role of AI in participatory democracy.

The surprise outcome of the election, with the BJP’s failure to win its predicted parliamentary majority, and India’s return to a deeply competitive political system especially highlights the possibility for AI to have a positive role in deliberative democracy and representative governance.

Lessons for the world’s democracies

It’s a goal of any political party or candidate in a democracy to have more targeted touch points with their constituents. The Indian elections have shown a unique attempt at using AI for more individualized communication across linguistically and ethnically diverse constituencies, and making their messages more accessible, especially to rural, low-income populations.

AI and the future of participatory democracy could make constituent communication not just personalized but also a dialogue, so voters can share their demands and experiences directly with their representatives—at speed and scale.

India can be an example of taking its recent fluency in AI-assisted party-to-people communications and moving it beyond politics. The government is already using these platforms to provide government services to citizens in their native languages.

If used safely and ethically, this technology could be an opportunity for a new era in representative governance, especially for the needs and experiences of people in rural areas to reach Parliament.

This essay was written with Vandinika Shukla and previously appeared in The Conversation.

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Posted on June 13, 2024 at 7:02 AM • View Comments

Using AI for Political Polling

Public polling is a critical function of modern political campaigns and movements, but it isn’t what it once was. Recent US election cycles have produced copious postmortems explaining both the successes and the flaws of public polling. There are two main reasons polling fails.

First, nonresponse has skyrocketed. It’s radically harder to reach people than it used to be. Few people fill out surveys that come in the mail anymore. Few people answer their phone when a stranger calls. Pew Research reported that 36% of the people they called in 1997 would talk to them, but only 6% by 2018. Pollsters worldwide have faced similar challenges.

Second, people don’t always tell pollsters what they really think. Some hide their true thoughts because they are embarrassed about them. Others behave as a partisan, telling the pollster what they think their party wants them to say—or what they know the other party doesn’t want to hear.

Despite these frailties, obsessive interest in polling nonetheless consumes our politics. Headlines more likely tout the latest changes in polling numbers than the policy issues at stake in the campaign. This is a tragedy for a democracy. We should treat elections like choices that have consequences for our lives and well-being, not contests to decide who gets which cushy job.

Polling Machines?

AI could change polling. AI can offer the ability to instantaneously survey and summarize the expressed opinions of individuals and groups across the web, understand trends by demographic, and offer extrapolations to new circumstances and policy issues on par with human experts. The politicians of the (near) future won’t anxiously pester their pollsters for information about the results of a survey fielded last week: they’ll just ask a chatbot what people think. This will supercharge our access to realtime, granular information about public opinion, but at the same time it might also exacerbate concerns about the quality of this information.

I know it sounds impossible, but stick with us.

Large language models, the AI foundations behind tools like ChatGPT, are built on top of huge corpuses of data culled from the Internet. These are models trained to recapitulate what millions of real people have written in response to endless topics, contexts, and scenarios. For a decade or more, campaigns have trawled social media, looking for hints and glimmers of how people are reacting to the latest political news. This makes asking questions of an AI chatbot similar in spirit to doing analytics on social media, except that they are generative: you can ask them new questions that no one has ever posted about before, you can generate more data from populations too small to measure robustly, and you can immediately ask clarifying questions of your simulated constituents to better understand their reasoning

Researchers and firms are already using LLMs to simulate polling results. Current techniques are based on the ideas of AI agents. An AI agent is an instance of an AI model that has been conditioned to behave in a certain way. For example, it may be primed to respond as if it is a person with certain demographic characteristics and can access news articles from certain outlets. Researchers have set up populations of thousands of AI agents that respond as if they are individual members of a survey population, like humans on a panel that get called periodically to answer questions.

The big difference between humans and AI agents is that the AI agents always pick up the phone, so to speak, no matter how many times you contact them. A political candidate or strategist can ask an AI agent whether voters will support them if they take position A versus B, or tweaks of those options, like policy A-1 versus A-2. They can ask that question of male voters versus female voters. They can further limit the query to married male voters of retirement age in rural districts of Illinois without college degrees who lost a job during the last recession; the AI will integrate as much context as you ask.

What’s so powerful about this system is that it can generalize to new scenarios and survey topics, and spit out a plausible answer, even if its accuracy is not guaranteed. In many cases, it will anticipate those responses at least as well as a human political expert. And if the results don’t make sense, the human can immediately prompt the AI with a dozen follow-up questions.

Making AI agents better polling subjects

When we ran our own experiments in this kind of AI use case with the earliest versions of the model behind ChatGPT (GPT-3.5), we found that it did a fairly good job at replicating human survey responses. The ChatGPT agents tended to match the responses of their human counterparts fairly well across a variety of survey questions, such as support for abortion and approval of the US Supreme Court. The AI polling results had average responses, and distributions across demographic properties such as age and gender, similar to real human survey panels.

Our major systemic failure happened on a question about US intervention in the Ukraine war.  In our experiments, the AI agents conditioned to be liberal were predominantly opposed to US intervention in Ukraine and likened it to the Iraq war. Conservative AI agents gave hawkish responses supportive of US intervention. This is pretty much what most political experts would have expected of the political equilibrium in US foreign policy at the start of the decade but was exactly wrong in the politics of today.

This mistake has everything to do with timing. The humans were asked the question after Russia’s full-scale invasion in 2022, whereas the AI model was trained using data that only covered events through September 2021. The AI got it wrong because it didn’t know how the politics had changed. The model lacked sufficient context on crucially relevant recent events.

We believe AI agents can overcome these shortcomings. While AI models are dependent on  the data they are trained with, and all the limitations inherent in that, what makes AI agents special is that they can automatically source and incorporate new data at the time they are asked a question. AI models can update the context in which they generate opinions by learning from the same sources that humans do. Each AI agent in a simulated panel can be exposed to the same social and media news sources as humans from that same demographic before they respond to a survey question. This works because AI agents can follow multi-step processes, such as reading a question, querying a defined database of information (such as Google, or the New York Times, or Fox News, or Reddit), and then answering a question.

In this way, AI polling tools can simulate exposing their synthetic survey panel to whatever news is most relevant to a topic and likely to emerge in each AI agent’s own echo chamber. And they can query for other relevant contextual information, such as demographic trends and historical data. Like human pollsters, they can try to refine their expectations on the basis of factors like how expensive homes are in a respondent’s neighborhood, or how many people in that district turned out to vote last cycle.

Likely use cases for AI polling

AI polling will be irresistible to campaigns, and to the media. But research is already revealing when and where this tool will fail. While AI polling will always have limitations in accuracy, that makes them similar to, not different from, traditional polling. Today’s pollsters are challenged to reach sample sizes large enough to measure statistically significant differences between similar populations, and the issues of nonresponse and inauthentic response can make them systematically wrong. Yet for all those shortcomings, both traditional and AI-based polls will still be useful. For all the hand-wringing and consternation over the accuracy of US political polling, national issue surveys still tend to be accurate to within a few percentage points. If you’re running for a town council seat or in a neck-and-neck national election, or just trying to make the right policy decision within a local government, you might care a lot about those small and localized differences. But if you’re looking to track directional changes over time, or differences between demographic groups, or to uncover insights about who responds best to what message, then these imperfect signals are sufficient to help campaigns and policymakers.

Where AI will work best is as an augmentation of more traditional human polls. Over time, AI tools will get better at anticipating human responses, and also at knowing when they will be most wrong or uncertain. They will recognize which issues and human communities are in the most flux, where the model’s training data is liable to steer it in the wrong direction. In those cases, AI models can send up a white flag and indicate that they need to engage human respondents to calibrate to real people’s perspectives. The AI agents can even be programmed to automate this. They can use existing survey tools—with all their limitations and latency—to query for authentic human responses when they need them.

This kind of human-AI polling chimera lands us, funnily enough, not too distant from where survey research is today. Decades of social science research has led to substantial innovations in statistical methodologies for analyzing survey data. Current polling methods already do substantial modeling and projecting to predictively model properties of a general population based on sparse survey samples. Today, humans fill out the surveys and computers fill in the gaps. In the future, it will be the opposite: AI will fill out the survey and, when the AI isn’t sure what box to check, humans will fill the gaps. So if you’re not comfortable with the idea that political leaders will turn to a machine to get intelligence about which candidates and policies you want, then you should have about as many misgivings about the present as you will the future.

And while the AI results could improve quickly, they probably won’t be seen as credible for some time. Directly asking people what they think feels more reliable than asking a computer what people think. We expect these AI-assisted polls will be initially used internally by campaigns, with news organizations relying on more traditional techniques. It will take a major election where AI is right and humans are wrong to change that.

This essay was written with Aaron Berger, Eric Gong, and Nathan Sanders, and previously appeared on the Harvard Kennedy School Ash Center’s website.

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Posted on June 12, 2024 at 7:02 AM • View Comments

LLMs Acting Deceptively

New research: “Deception abilities emerged in large language models“:

Abstract: Large language models (LLMs) are currently at the forefront of intertwining AI systems with human communication and everyday life. Thus, aligning them with human values is of great importance. However, given the steady increase in reasoning abilities, future LLMs are under suspicion of becoming able to deceive human operators and utilizing this ability to bypass monitoring efforts. As a prerequisite to this, LLMs need to possess a conceptual understanding of deception strategies. This study reveals that such strategies emerged in state-of-the-art LLMs, but were nonexistent in earlier LLMs. We conduct a series of experiments showing that state-of-the-art LLMs are able to understand and induce false beliefs in other agents, that their performance in complex deception scenarios can be amplified utilizing chain-of-thought reasoning, and that eliciting Machiavellianism in LLMs can trigger misaligned deceptive behavior. GPT-4, for instance, exhibits deceptive behavior in simple test scenarios 99.16% of the time (P < 0.001). In complex second-order deception test scenarios where the aim is to mislead someone who expects to be deceived, GPT-4 resorts to deceptive behavior 71.46% of the time (P < 0.001) when augmented with chain-of-thought reasoning. In sum, revealing hitherto unknown machine behavior in LLMs, our study contributes to the nascent field of machine psychology.

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Posted on June 11, 2024 at 7:02 AM • View Comments

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Sidebar photo of Bruce Schneier by Joe MacInnis.