Tag Archives: laws

Using AI-Generated Legislative Amendments as a Delaying Technique

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2024/04/using-ai-generated-legislative-amendments-as-a-delaying-technique.html

Canadian legislators proposed 19,600 amendments—almost certainly AI-generated—to a bill in an attempt to delay its adoption.

I wrote about many different legislative delaying tactics in A Hacker’s Mind, but this is a new one.

How the “Frontier” Became the Slogan of Uncontrolled AI

Post Syndicated from B. Schneier original https://www.schneier.com/blog/archives/2024/02/how-the-frontier-became-the-slogan-of-uncontrolled-ai.html

Artificial intelligence (AI) has been billed as the next frontier of humanity: the newly available expanse whose exploration will drive the next era of growth, wealth, and human flourishing. It’s a scary metaphor. Throughout American history, the drive for expansion and the very concept of terrain up for grabs—land grabs, gold rushes, new frontiers—have provided a permission structure for imperialism and exploitation. This could easily hold true for AI.

This isn’t the first time the concept of a frontier has been used as a metaphor for AI, or technology in general. As early as 2018, the powerful foundation models powering cutting-edge applications like chatbots have been called “frontier AI.” In previous decades, the internet itself was considered an electronic frontier. Early cyberspace pioneer John Perry Barlow wrote “Unlike previous frontiers, this one has no end.” When he and others founded the internet’s most important civil liberties organization, they called it the Electronic Frontier Foundation.

America’s experience with frontiers is fraught, to say the least. Expansion into the Western frontier and beyond has been a driving force in our country’s history and identity—and has led to some of the darkest chapters of our past. The tireless drive to conquer the frontier has directly motivated some of this nation’s most extreme episodes of racism, imperialism, violence, and exploitation.

That history has something to teach us about the material consequences we can expect from the promotion of AI today. The race to build the next great AI app is not the same as the California gold rush. But the potential that outsize profits will warp our priorities, values, and morals is, unfortunately, analogous.

Already, AI is starting to look like a colonialist enterprise. AI tools are helping the world’s largest tech companies grow their power and wealth, are spurring nationalistic competition between empires racing to capture new markets, and threaten to supercharge government surveillance and systems of apartheid. It looks more than a bit like the competition among colonialist state and corporate powers in the seventeenth century, which together carved up the globe and its peoples. By considering America’s past experience with frontiers, we can understand what AI may hold for our future, and how to avoid the worst potential outcomes.

America’s “Frontier” Problem

For 130 years, historians have used frontier expansion to explain sweeping movements in American history. Yet only for the past thirty years have we generally acknowledged its disastrous consequences.

Frederick Jackson Turner famously introduced the frontier as a central concept for understanding American history in his vastly influential 1893 essay. As he concisely wrote, “American history has been in a large degree the history of the colonization of the Great West.”

Turner used the frontier to understand all the essential facts of American life: our culture, way of government, national spirit, our position among world powers, even the “struggle” of slavery. The endless opportunity for westward expansion was a beckoning call that shaped the American way of life. Per Turner’s essay, the frontier resulted in the individualistic self-sufficiency of the settler and gave every (white) man the opportunity to attain economic and political standing through hardscrabble pioneering across dangerous terrain.The New Western History movement, gaining steam through the 1980s and led by researchers like Patricia Nelson Limerick, laid plain the racial, gender, and class dynamics that were always inherent to the frontier narrative. This movement’s story is one where frontier expansion was a tool used by the white settler to perpetuate a power advantage.The frontier was not a siren calling out to unwary settlers; it was a justification, used by one group to subjugate another. It was always a convenient, seemingly polite excuse for the powerful to take what they wanted. Turner grappled with some of the negative consequences and contradictions of the frontier ethic and how it shaped American democracy. But many of those whom he influenced did not do this; they celebrated it as a feature, not a bug. Theodore Roosevelt wrote extensively and explicitly about how the frontier and his conception of white supremacy justified expansion to points west and, through the prosecution of the Spanish-American War, far across the Pacific. Woodrow Wilson, too, celebrated the imperial loot from that conflict in 1902. Capitalist systems are “addicted to geographical expansion” and even, when they run out of geography, seek to produce new kinds of spaces to expand into. This is what the geographer David Harvey calls the “spatial fix.”Claiming that AI will be a transformative expanse on par with the Louisiana Purchase or the Pacific frontiers is a bold assertion—but increasingly plausible after a year dominated by ever more impressive demonstrations of generative AI tools. It’s a claim bolstered by billions of dollars in corporate investment, by intense interest of regulators and legislators worldwide in steering how AI is developed and used, and by the variously utopian or apocalyptic prognostications from thought leaders of all sectors trying to understand how AI will shape their sphere—and the entire world.

AI as a Permission Structure

Like the western frontier in the nineteenth century, the maniacal drive to unlock progress via advancement in AI can become a justification for political and economic expansionism and an excuse for racial oppression.

In the modern day, OpenAI famously paid dozens of Kenyans little more than a dollar an hour to process data used in training their models underlying products such as ChatGPT. Paying low wages to data labelers surely can’t be equated to the chattel slavery of nineteenth-century America. But these workers did endure brutal conditions, including being set to constantly review content with “graphic scenes of violence, self-harm, murder, rape, necrophilia, child abuse, bestiality, and incest.” There is a global market for this kind of work, which has been essential to the most important recent advances in AI such as Reinforcement Learning with Human Feedback, heralded as the most important breakthrough of ChatGPT.

The gold rush mentality associated with expansion is taken by the new frontiersmen as permission to break the rules, and to build wealth at the expense of everyone else. In 1840s California, gold miners trespassed on public lands and yet were allowed to stake private claims to the minerals they found, and even to exploit the water rights on those lands. Again today, the game is to push the boundaries on what rule-breaking society will accept, and hope that the legal system can’t keep up.

Many internet companies have behaved in exactly the same way since the dot-com boom. The prospectors of internet wealth lobbied for, or simply took of their own volition, numerous government benefits in their scramble to capture those frontier markets. For years, the Federal Trade Commission has looked the other way or been lackadaisical in halting antitrust abuses by Amazon, Facebook, and Google. Companies like Uber and Airbnb exploited loopholes in, or ignored outright, local laws on taxis and hotels. And Big Tech platforms enjoyed a liability shield that protected them from punishment the contents people posted to their sites.

We can already see this kind of boundary pushing happening with AI.

Modern frontier AI models are trained using data, often copyrighted materials, with untested legal justification. Data is like water for AI, and, like the fight over water rights in the West, we are repeating a familiar process of public acquiescence to private use of resources. While some lawsuits are pending, so far AI companies have faced no significant penalties for the unauthorized use of this data.

Pioneers of self-driving vehicles tried to skip permitting processes and used fake demonstrations of their capabilities to avoid government regulation and entice consumers. Meanwhile, AI companies’ hope is that they won’t be held to blame if the AI tools they produce spew out harmful content that causes damage in the real world. They are trying to use the same liability shield that fostered Big Tech’s exploitation of the previous electronic frontiers—the web and social media—to protect their own actions.

Even where we have concrete rules governing deleterious behavior, some hope that using AI is itself enough to skirt them. Copyright infringement is illegal if a person does it, but would that same person be punished if they train a large language model to regurgitate copyrighted works? In the political sphere, the Federal Election Commission has precious few powers to police political advertising; some wonder if they simply won’t be considered relevant if people break those rules using AI.

AI and American Exceptionalism

Like The United States’ historical frontier, AI has a feel of American exceptionalism. Historically, we believed we were different from the Old World powers of Europe because we enjoyed the manifest destiny of unrestrained expansion between the oceans. Today, we have the most CPU power, the most data scientists, the most venture-capitalist investment, and the most AI companies. This exceptionalism has historically led many Americans to believe they don’t have to play by the same rules as everyone else.

Both historically and in the modern day, this idea has led to deleterious consequences such as militaristic nationalism (leading to justifying of foreign interventions in Iraq and elsewhere), masking of severe inequity within our borders, abdication of responsibility from global treaties on climate and law enforcement, and alienation from the international community. American exceptionalism has also wrought havoc on our country’s engagement with the internet, including lawless spying and surveillance by forces like the National Security Agency.

The same line of thinking could have disastrous consequences if applied to AI. It could perpetuate a nationalistic, Cold War–style narrative about America’s inexorable struggle with China, this time predicated on an AI arms race. Moral exceptionalism justifies why we should be allowed to use tools and weapons that are dangerous in the hands of a competitor, or enemy. It could enable the next stage of growth of the military-industrial complex, with claims of an urgent need to modernize missile systems and drones through using AI. And it could renew a rationalization for violating civil liberties in the US and human rights abroad, empowered by the idea that racial profiling is more objective if enforced by computers.The inaction of Congress on AI regulation threatens to land the US in a regime of de facto American exceptionalism for AI. While the EU is about to pass its comprehensive AI Act, lobbyists in the US have muddled legislative action. While the Biden administration has used its executive authority and federal purchasing power to exert some limited control over AI, the gap left by lack of legislation leaves AI in the US looking like the Wild West—a largely unregulated frontier.The lack of restraint by the US on potentially dangerous AI technologies has a global impact. First, its tech giants let loose their products upon the global public, with the harms that this brings with it. Second, it creates a negative incentive for other jurisdictions to more forcefully regulate AI. The EU’s regulation of high-risk AI use cases begins to look like unilateral disarmament if the US does not take action itself. Why would Europe tie the hands of its tech competitors if the US refuses to do the same?

AI and Unbridled Growth

The fundamental problem with frontiers is that they seem to promise cost-free growth. There was a constant pressure for American westward expansion because a bigger, more populous country accrues more power and wealth to the elites and because, for any individual, a better life was always one more wagon ride away into “empty” terrain. AI presents the same opportunities. No matter what field you’re in or what problem you’re facing, the attractive opportunity of AI as a free labor multiplier probably seems like the solution; or, at least, makes for a good sales pitch.

That would actually be okay, except that the growth isn’t free. America’s imperial expansion displaced, harmed, and subjugated native peoples in the Americas, Africa, and the Pacific, while enlisting poor whites to participate in the scheme against their class interests. Capitalism makes growth look like the solution to all problems, even when it’s clearly not. The problem is that so many costs are externalized. Why pay a living wage to human supervisors training AI models when an outsourced gig worker will do it at a fraction of the cost? Why power data centers with renewable energy when it’s cheaper to surge energy production with fossil fuels? And why fund social protections for wage earners displaced by automation if you don’t have to? The potential of consumer applications of AI, from personal digital assistants to self-driving cars, is irresistible; who wouldn’t want a machine to take on the most routinized and aggravating tasks in your daily life? But the externalized cost for consumers is accepting the inevitability of domination by an elite who will extract every possible profit from AI services.

Controlling Our Frontier Impulses

None of these harms are inevitable. Although the structural incentives of capitalism and its growth remain the same, we can make different choices about how to confront them.

We can strengthen basic democratic protections and market regulations to avoid the worst impacts of AI colonialism. We can require ethical employment for the humans toiling to label data and train AI models. And we can set the bar higher for mitigating bias in training and harm from outputs of AI models.

We don’t have to cede all the power and decision making about AI to private actors. We can create an AI public option to provide an alternative to corporate AI. We can provide universal access to ethically built and democratically governed foundational AI models that any individual—or company—could use and build upon.

More ambitiously, we can choose not to privatize the economic gains of AI. We can cap corporate profits, raise the minimum wage, or redistribute an automation dividend as a universal basic income to let everyone share in the benefits of the AI revolution. And, if these technologies save as much labor as companies say they do, maybe we can also all have some of that time back.

And we don’t have to treat the global AI gold rush as a zero-sum game. We can emphasize international cooperation instead of competition. We can align on shared values with international partners and create a global floor for responsible regulation of AI. And we can ensure that access to AI uplifts developing economies instead of further marginalizing them.

This essay was written with Nathan Sanders, and was originally published in Jacobin.

Ten Ways AI Will Change Democracy

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2023/11/ten-ways-ai-will-change-democracy.html

Artificial intelligence will change so many aspects of society, largely in ways that we cannot conceive of yet. Democracy, and the systems of governance that surround it, will be no exception. In this short essay, I want to move beyond the “AI-generated disinformation” trope and speculate on some of the ways AI will change how democracy functions—in both large and small ways.

When I survey how artificial intelligence might upend different aspects of modern society, democracy included, I look at four different dimensions of change: speed, scale, scope, and sophistication. Look for places where changes in degree result in changes of kind. Those are where the societal upheavals will happen.

Some items on my list are still speculative, but none require science-fictional levels of technological advance. And we can see the first stages of many of them today. When reading about the successes and failures of AI systems, it’s important to differentiate between the fundamental limitations of AI as a technology, and the practical limitations of AI systems in the fall of 2023. Advances are happening quickly, and the impossible is becoming the routine. We don’t know how long this will continue, but my bet is on continued major technological advances in the coming years. Which means it’s going to be a wild ride.

So, here’s my list:

  1. AI as educator. We are already seeing AI serving the role of teacher. It’s much more effective for a student to learn a topic from an interactive AI chatbot than from a textbook. This has applications for democracy. We can imagine chatbots teaching citizens about different issues, such as climate change or tax policy. We can imagine candidates deploying chatbots of themselves, allowing voters to directly engage with them on various issues. A more general chatbot could know the positions of all the candidates, and help voters decide which best represents their position. There are a lot of possibilities here.
  2. AI as sense maker. There are many areas of society where accurate summarization is important. Today, when constituents write to their legislator, those letters get put into two piles—one for and another against—and someone compares the height of those piles. AI can do much better. It can provide a rich summary of the comments. It can help figure out which are unique and which are form letters. It can highlight unique perspectives. This same system can also work for comments to different government agencies on rulemaking processes—and on documents generated during the discovery process in lawsuits.
  3. AI as moderator, mediator, and consensus builder. Imagine online conversations in which AIs serve the role of moderator. This could ensure that all voices are heard. It could block hateful—or even just off-topic—comments. It could highlight areas of agreement and disagreement. It could help the group reach a decision. This is nothing that a human moderator can’t do, but there aren’t enough human moderators to go around. AI can give this capability to every decision-making group. At the extreme, an AI could be an arbiter—a judge—weighing evidence and making a decision. These capabilities don’t exist yet, but they are not far off.
  4. AI as lawmaker. We have already seen proposed legislation written by AI, albeit more as a stunt than anything else. But in the future AIs will help craft legislation, dealing with the complex ways laws interact with each other. More importantly, AIs will eventually be able to craft loopholes in legislation, ones potentially too complicated for people to easily notice. On the other side of that, AIs could be used to find loopholes in legislation—for both existing and pending laws. And more generally, AIs could be used to help develop policy positions.
  5. AI as political strategist. Right now, you can ask your favorite chatbot questions about political strategy: what legislation would further your political goals, what positions to publicly take, what campaign slogans to use. The answers you get won’t be very good, but that’ll improve with time. In the future we should expect politicians to make use of this AI expertise: not to follow blindly, but as another source of ideas. And as AIs become more capable at using tools, they can automatically conduct polls and focus groups to test out political ideas. There are a lot of possibilities here. AIs could also engage in fundraising campaigns, directly soliciting contributions from people.
  6. AI as lawyer. We don’t yet know which aspects of the legal profession can be done by AIs, but many routine tasks that are now handled by attorneys will soon be able to be completed by an AI. Early attempts at having AIs write legal briefs haven’t worked, but this will change as the systems get better at accuracy. Additionally, AIs can help people navigate government systems: filling out forms, applying for services, contesting bureaucratic actions. And future AIs will be much better at writing legalese, reducing the cost of legal counsel.
  7. AI as cheap reasoning generator. More generally, AI chatbots are really good at generating persuasive arguments. Today, writing out a persuasive argument takes time and effort, and our systems reflect that. We can easily imagine AIs conducting lobbying campaigns, generating and submitting comments on legislation and rulemaking. This also has applications for the legal system. For example: if it is suddenly easy to file thousands of court cases, this will overwhelm the courts. Solutions for this are hard. We could increase the cost of filing a court case, but that becomes a burden on the poor. The only solution might be another AI working for the court, dealing with the deluge of AI-filed cases—which doesn’t sound like a great idea.
  8. AI as law enforcer. Automated systems already act as law enforcement in some areas: speed trap cameras are an obvious example. AI can take this kind of thing much further, automatically identifying people who cheat on tax returns or when applying for government services. This has the obvious problem of false positives, which could be hard to contest if the courts believe that “the computer is always right.” Separately, future laws might be so complicated that only AIs are able to decide whether or not they are being broken. And, like breathalyzers, defendants might not be allowed to know how they work.
  9. AI as propagandist. AIs can produce and distribute propaganda faster than humans can. This is an obvious risk, but we don’t know how effective any of it will be. It makes disinformation campaigns easier, which means that more people will take advantage of them. But people will be more inured against the risks. More importantly, AI’s ability to summarize and understand text can enable much more effective censorship.
  10. AI as political proxy. Finally, we can imagine an AI voting on behalf of individuals. A voter could feed an AI their social, economic, and political preferences; or it can infer them by listening to them talk and watching their actions. And then it could be empowered to vote on their behalf, either for others who would represent them, or directly on ballot initiatives. On the one hand, this would greatly increase voter participation. On the other hand, it would further disengage people from the act of understanding politics and engaging in democracy.

When I teach AI policy at HKS, I stress the importance of separating the specific AI chatbot technologies in November of 2023 with AI’s technological possibilities in general. Some of the items on my list will soon be possible; others will remain fiction for many years. Similarly, our acceptance of these technologies will change. Items on that list that we would never accept today might feel routine in a few years. A judgeless courtroom seems crazy today, but so did a driverless car a few years ago. Don’t underestimate our ability to normalize new technologies. My bet is that we’re in for a wild ride.

This essay previously appeared on the Harvard Kennedy School Ash Center’s website.

Child Exploitation and the Crypto Wars

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2023/10/child-exploitation-and-the-crypto-wars.html

Susan Landau published an excellent essay on the current justification for the government breaking end-to-end-encryption: child sexual abuse and exploitation (CSAE). She puts the debate into historical context, discusses the problem of CSAE, and explains why breaking encryption isn’t the solution.

AI and US Election Rules

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2023/10/ai-and-us-election-rules.html

If an AI breaks the rules for you, does that count as breaking the rules? This is the essential question being taken up by the Federal Election Commission this month, and public input is needed to curtail the potential for AI to take US campaigns (even more) off the rails.

At issue is whether candidates using AI to create deepfaked media for political advertisements should be considered fraud or legitimate electioneering. That is, is it allowable to use AI image generators to create photorealistic images depicting Trump hugging Anthony Fauci? And is it allowable to use dystopic images generated by AI in political attack ads?

For now, the answer to these questions is probably “yes.” These are fairly innocuous uses of AI, not any different than the old-school approach of hiring actors and staging a photoshoot, or using video editing software. Even in cases where AI tools will be put to scurrilous purposes, that’s probably legal in the US system. Political ads are, after all, a medium in which you are explicitly permitted to lie.

The concern over AI is a distraction, but one that can help draw focus to the real issue. What matters isn’t how political content is generated; what matters is the content itself and how it is distributed.

Future uses of AI by campaigns go far beyond deepfaked images. Campaigns will also use AI to personalize communications. Whereas the previous generation of social media microtargeting was celebrated for helping campaigns reach a precision of thousands or hundreds of voters, the automation offered by AI will allow campaigns to tailor their advertisements and solicitations to the individual.

Most significantly, AI will allow digital campaigning to evolve from a broadcast medium to an interactive one. AI chatbots representing campaigns are capable of responding to questions instantly and at scale, like a town hall taking place in every voter’s living room, simultaneously. Ron DeSantis’ presidential campaign has reportedly already started using OpenAI’s technology to handle text message replies to voters.

At the same time, it’s not clear whose responsibility it is to keep US political advertisements grounded in reality—if it is anyone’s. The FEC’s role is campaign finance, and is further circumscribed by the Supreme Court’s repeated stripping of its authorities. The Federal Communications Commission has much more expansive responsibility for regulating political advertising in broadcast media, as well as political robocalls and text communications. However, the FCC hasn’t done much in recent years to curtail political spam. The Federal Trade Commission enforces truth in advertising standards, but political campaigns have been largely exempted from these requirements on First Amendment grounds.

To further muddy the waters, much of the online space remains loosely regulated, even as campaigns have fully embraced digital tactics. There are still insufficient disclosure requirements for digital ads. Campaigns pay influencers to post on their behalf to circumvent paid advertising rules. And there are essentially no rules beyond the simple use of disclaimers for videos that campaigns post organically on their own websites and social media accounts, even if they are shared millions of times by others.

Almost everyone has a role to play in improving this situation.

Let’s start with the platforms. Google announced earlier this month that it would require political advertisements on YouTube and the company’s other advertising platforms to disclose when they use AI images, audio, and video that appear in their ads. This is to be applauded, but we cannot rely on voluntary actions by private companies to protect our democracy. Such policies, even when well-meaning, will be inconsistently devised and enforced.

The FEC should use its limited authority to stem this coming tide. The FEC’s present consideration of rulemaking on this issue was prompted by Public Citizen, which petitioned the Commission to "clarify that the law against ‘fraudulent misrepresentation’ (52 U.S.C. §30124) applies to deliberately deceptive AI-produced content in campaign communications." The FEC’s regulation against fraudulent misrepresentation (C.F.R. §110.16) is very narrow; it simply restricts candidates from pretending to be speaking on behalf of their opponents in a “damaging” way.

Extending this to explicitly cover deepfaked AI materials seems appropriate. We should broaden the standards to robustly regulate the activity of fraudulent misrepresentation, whether the entity performing that activity is AI or human—but this is only the first step. If the FEC takes up rulemaking on this issue, it could further clarify what constitutes “damage.” Is it damaging when a PAC promoting Ron DeSantis uses an AI voice synthesizer to generate a convincing facsimile of the voice of his opponent Donald Trump speaking his own Tweeted words? That seems like fair play. What if opponents find a way to manipulate the tone of the speech in a way that misrepresents its meaning? What if they make up words to put in Trump’s mouth? Those use cases seem to go too far, but drawing the boundaries between them will be challenging.

Congress has a role to play as well. Senator Klobuchar and colleagues have been promoting both the existing Honest Ads Act and the proposed REAL Political Ads Act, which would expand the FEC’s disclosure requirements for content posted on the Internet and create a legal requirement for campaigns to disclose when they have used images or video generated by AI in political advertising. While that’s worthwhile, it focuses on the shiny object of AI and misses the opportunity to strengthen law around the underlying issues. The FEC needs more authority to regulate campaign spending on false or misleading media generated by any means and published to any outlet. Meanwhile, the FEC’s own Inspector General continues to warn Congress that the agency is stressed by flat budgets that don’t allow it to keep pace with ballooning campaign spending.

It is intolerable for such a patchwork of commissions to be left to wonder which, if any of them, has jurisdiction to act in the digital space. Congress should legislate to make clear that there are guardrails on political speech and to better draw the boundaries between the FCC, FEC, and FTC’s roles in governing political speech. While the Supreme Court cannot be relied upon to uphold common sense regulations on campaigning, there are strategies for strengthening regulation under the First Amendment. And Congress should allocate more funding for enforcement.

The FEC has asked Congress to expand its jurisdiction, but no action is forthcoming. The present Senate Republican leadership is seen as an ironclad barrier to expanding the Commission’s regulatory authority. Senate Majority Leader Mitch McConnell has a decades-long history of being at the forefront of the movement to deregulate American elections and constrain the FEC. In 2003, he brought the unsuccessful Supreme Court case against the McCain-Feingold campaign finance reform act (the one that failed before the Citizens United case succeeded).

The most impactful regulatory requirement would be to require disclosure of interactive applications of AI for campaigns—and this should fall under the remit of the FCC. If a neighbor texts me and urges me to vote for a candidate, I might find that meaningful. If a bot does it under the instruction of a campaign, I definitely won’t. But I might find a conversation with the bot—knowing it is a bot—useful to learn about the candidate’s platform and positions, as long as I can be confident it is going to give me trustworthy information.

The FCC should enter rulemaking to expand its authority for regulating peer-to-peer (P2P) communications to explicitly encompass interactive AI systems. And Congress should pass enabling legislation to back it up, giving it authority to act not only on the SMS text messaging platform, but also over the wider Internet, where AI chatbots can be accessed over the web and through apps.

And the media has a role. We can still rely on the media to report out what videos, images, and audio recordings are real or fake. Perhaps deepfake technology makes it impossible to verify the truth of what is said in private conversations, but this was always unstable territory.

What is your role? Those who share these concerns can submit a comment to the FEC’s open public comment process before October 16, urging it to use its available authority. We all know government moves slowly, but a show of public interest is necessary to get the wheels moving.

Ultimately, all these policy changes serve the purpose of looking beyond the shiny distraction of AI to create the authority to counter bad behavior by humans. Remember: behind every AI is a human who should be held accountable.

This essay was written with Nathan Sanders, and was previously published on the Ash Center website.

AI and Microdirectives

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2023/07/ai-and-microdirectives.html

Imagine a future in which AIs automatically interpret—and enforce—laws.

All day and every day, you constantly receive highly personalized instructions for how to comply with the law, sent directly by your government and law enforcement. You’re told how to cross the street, how fast to drive on the way to work, and what you’re allowed to say or do online—if you’re in any situation that might have legal implications, you’re told exactly what to do, in real time.

Imagine that the computer system formulating these personal legal directives at mass scale is so complex that no one can explain how it reasons or works. But if you ignore a directive, the system will know, and it’ll be used as evidence in the prosecution that’s sure to follow.

This future may not be far off—automatic detection of lawbreaking is nothing new. Speed cameras and traffic-light cameras have been around for years. These systems automatically issue citations to the car’s owner based on the license plate. In such cases, the defendant is presumed guilty unless they prove otherwise, by naming and notifying the driver.

In New York, AI systems equipped with facial recognition technology are being used by businesses to identify shoplifters. Similar AI-powered systems are being used by retailers in Australia and the United Kingdom to identify shoplifters and provide real-time tailored alerts to employees or security personnel. China is experimenting with even more powerful forms of automated legal enforcement and targeted surveillance.

Breathalyzers are another example of automatic detection. They estimate blood alcohol content by calculating the number of alcohol molecules in the breath via an electrochemical reaction or infrared analysis (they’re basically computers with fuel cells or spectrometers attached). And they’re not without controversy: Courts across the country have found serious flaws and technical deficiencies with Breathalyzer devices and the software that powers them. Despite this, criminal defendants struggle to obtain access to devices or their software source code, with Breathalyzer companies and courts often refusing to grant such access. In the few cases where courts have actually ordered such disclosures, that has usually followed costly legal battles spanning many years.

AI is about to make this issue much more complicated, and could drastically expand the types of laws that can be enforced in this manner. Some legal scholars predict that computationally personalized law and its automated enforcement are the future of law. These would be administered by what Anthony Casey and Anthony Niblett call “microdirectives,” which provide individualized instructions for legal compliance in a particular scenario.

Made possible by advances in surveillance, communications technologies, and big-data analytics, microdirectives will be a new and predominant form of law shaped largely by machines. They are “micro” because they are not impersonal general rules or standards, but tailored to one specific circumstance. And they are “directives” because they prescribe action or inaction required by law.

A Digital Millennium Copyright Act takedown notice is a present-day example of a microdirective. The DMCA’s enforcement is almost fully automated, with copyright “bots” constantly scanning the internet for copyright-infringing material, and automatically sending literally hundreds of millions of DMCA takedown notices daily to platforms and users. A DMCA takedown notice is tailored to the recipient’s specific legal circumstances. It also directs action—remove the targeted content or prove that it’s not infringing—based on the law.

It’s easy to see how the AI systems being deployed by retailers to identify shoplifters could be redesigned to employ microdirectives. In addition to alerting business owners, the systems could also send alerts to the identified persons themselves, with tailored legal directions or notices.

A future where AIs interpret, apply, and enforce most laws at societal scale like this will exponentially magnify problems around fairness, transparency, and freedom. Forget about software transparency—well-resourced AI firms, like Breathalyzer companies today, would no doubt ferociously guard their systems for competitive reasons. These systems would likely be so complex that even their designers would not be able to explain how the AIs interpret and apply the law—something we’re already seeing with today’s deep learning neural network systems, which are unable to explain their reasoning.

Even the law itself could become hopelessly vast and opaque. Legal microdirectives sent en masse for countless scenarios, each representing authoritative legal findings formulated by opaque computational processes, could create an expansive and increasingly complex body of law that would grow ad infinitum.

And this brings us to the heart of the issue: If you’re accused by a computer, are you entitled to review that computer’s inner workings and potentially challenge its accuracy in court? What does cross-examination look like when the prosecutor’s witness is a computer? How could you possibly access, analyze, and understand all microdirectives relevant to your case in order to challenge the AI’s legal interpretation? How could courts hope to ensure equal application of the law? Like the man from the country in Franz Kafka’s parable in The Trial, you’d die waiting for access to the law, because the law is limitless and incomprehensible.

This system would present an unprecedented threat to freedom. Ubiquitous AI-powered surveillance in society will be necessary to enable such automated enforcement. On top of that, research—including empirical studies conducted by one of us (Penney)—has shown that personalized legal threats or commands that originate from sources of authority—state or corporate—can have powerful chilling effects on people’s willingness to speak or act freely. Imagine receiving very specific legal instructions from law enforcement about what to say or do in a situation: Would you feel you had a choice to act freely?

This is a vision of AI’s invasive and Byzantine law of the future that chills to the bone. It would be unlike any other law system we’ve seen before in human history, and far more dangerous for our freedoms. Indeed, some legal scholars argue that this future would effectively be the death of law.

Yet it is not a future we must endure. Proposed bans on surveillance technology like facial recognition systems can be expanded to cover those enabling invasive automated legal enforcement. Laws can mandate interpretability and explainability for AI systems to ensure everyone can understand and explain how the systems operate. If a system is too complex, maybe it shouldn’t be deployed in legal contexts. Enforcement by personalized legal processes needs to be highly regulated to ensure oversight, and should be employed only where chilling effects are less likely, like in benign government administration or regulatory contexts where fundamental rights and freedoms are not at risk.

AI will inevitably change the course of law. It already has. But we don’t have to accept its most extreme and maximal instantiations, either today or tomorrow.

This essay was written with Jon Penney, and previously appeared on Slate.com.

Wisconsin Governor Hacks the Veto Process

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2023/07/wisconsin-governor-hacks-the-veto-process.html

In my latest book, A Hacker’s Mind, I wrote about hacks as loophole exploiting. This is a great example: The Wisconsin governor used his line-item veto powers—supposedly unique in their specificity—to change a one-year funding increase into a 400-year funding increase.

He took this wording:

Section 402. 121.905 (3) (c) 9. of the statues is created to read: 121.903 (3) (c) 9. For the limit for the 2023-24 school year and the 2024-25 school year, add $325 to the result under par. (b).

And he deleted these words, numbers, and punctuation marks:

Section 402. 121.905 (3) (c) 9. of the statues is created to read: 121.903 (3) (c) 9. For the limit for the 2023-24 school year and the 2024–25 school year, add $325 to the result under par. (b).

Seems to be legal:

Rick Champagne, director and general counsel of the nonpartisan Legislative Reference Bureau, said Evers’ 400-year veto is lawful in terms of its form because the governor vetoed words and digits.

“Both are allowable under the constitution and court decisions on partial veto. The hyphen seems to be new, but the courts have allowed partial veto of punctuation,” Champagne said.

Definitely a hack. This is not what anyone thinks about when they imagine using a line-item veto.

And it’s not the first time. I don’t know the details, but this was certainly the same sort of character-by-character editing:

Mr Evers’ Republican predecessor once deploying it to extend a state programme’s deadline by one thousand years.

A couple of other things:

One, this isn’t really a 400-year change. Yes, that’s what the law says. But it can be repealed. And who knows that a dollar will be worth—or if they will even be used—that many decades from now.

And two, from now all Wisconsin lawmakers will have to be on the alert for this sort of thing. All contentious bills will be examined for the possibility of this sort of delete-only rewriting. This sentence could have been reworded, for example:

For the 2023-2025 school years, add $325 to the result under par. (b).

The problem is, of course, that legalese developed over the centuries to be extra wordy in order to limit disputes. If lawmakers need to state things in the minimal viable language, that will increase court battles later. And that’s not even enough. Bills can be thousands of words long. If any arbitrary characters can be glued together by deleting enough other characters, bills can say anything the governor wants.

The real solution is to return the line-item veto to what we all think it is: the ability to remove individual whole provisions from a law before signing it.

AI as Sensemaking for Public Comments

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2023/06/ai-as-sensemaking-for-public-comments.html

It’s become fashionable to think of artificial intelligence as an inherently dehumanizing technology, a ruthless force of automation that has unleashed legions of virtual skilled laborers in faceless form. But what if AI turns out to be the one tool able to identify what makes your ideas special, recognizing your unique perspective and potential on the issues where it matters most?

You’d be forgiven if you’re distraught about society’s ability to grapple with this new technology. So far, there’s no lack of prognostications about the democratic doom that AI may wreak on the US system of government. There are legitimate reasons to be concerned that AI could spread misinformation, break public comment processes on regulations, inundate legislators with artificial constituent outreach, help to automate corporate lobbying, or even generate laws in a way tailored to benefit narrow interests.

But there are reasons to feel more sanguine as well. Many groups have started demonstrating the potential beneficial uses of AI for governance. A key constructive-use case for AI in democratic processes is to serve as discussion moderator and consensus builder.

To help democracy scale better in the face of growing, increasingly interconnected populations—as well as the wide availability of AI language tools that can generate reams of text at the click of a button—the US will need to leverage AI’s capability to rapidly digest, interpret and summarize this content.

There are two different ways to approach the use of generative AI to improve civic participation and governance. Each is likely to lead to drastically different experience for public policy advocates and other people trying to have their voice heard in a future system where AI chatbots are both the dominant readers and writers of public comment.

For example, consider individual letters to a representative, or comments as part of a regulatory rulemaking process. In both cases, we the people are telling the government what we think and want.

For more than half a century, agencies have been using human power to read through all the comments received, and to generate summaries and responses of their major themes. To be sure, digital technology has helped.

In 2021, the Council of Federal Chief Data Officers recommended modernizing the comment review process by implementing natural language processing tools for removing duplicates and clustering similar comments in processes governmentwide. These tools are simplistic by the standards of 2023 AI. They work by assessing the semantic similarity of comments based on metrics like word frequency (How often did you say “personhood”?) and clustering similar comments and giving reviewers a sense of what topic they relate to.

Think of this approach as collapsing public opinion. They take a big, hairy mass of comments from thousands of people and condense them into a tidy set of essential reading that generally suffices to represent the broad themes of community feedback. This is far easier for a small agency staff or legislative office to handle than it would be for staffers to actually read through that many individual perspectives.

But what’s lost in this collapsing is individuality, personality, and relationships. The reviewer of the condensed comments may miss the personal circumstances that led so many commenters to write in with a common point of view, and may overlook the arguments and anecdotes that might be the most persuasive content of the testimony.

Most importantly, the reviewers may miss out on the opportunity to recognize committed and knowledgeable advocates, whether interest groups or individuals, who could have long-term, productive relationships with the agency.

These drawbacks have real ramifications for the potential efficacy of those thousands of individual messages, undermining what all those people were doing it for. Still, practicality tips the balance toward of some kind of summarization approach. A passionate letter of advocacy doesn’t hold any value if regulators or legislators simply don’t have time to read it.

There is another approach. In addition to collapsing testimony through summarization, government staff can use modern AI techniques to explode it. They can automatically recover and recognize a distinctive argument from one piece of testimony that does not exist in the thousands of other testimonies received. They can discover the kinds of constituent stories and experiences that legislators love to repeat at hearings, town halls and campaign events. This approach can sustain the potential impact of individual public comment to shape legislation even as the volumes of testimony may rise exponentially.

In computing, there is a rich history of that type of automation task in what is called outlier detection. Traditional methods generally involve finding a simple model that explains most of the data in question, like a set of topics that well describe the vast majority of submitted comments. But then they go a step further by isolating those data points that fall outside the mold—comments that don’t use arguments that fit into the neat little clusters.

State-of-the-art AI language models aren’t necessary for identifying outliers in text document data sets, but using them could bring a greater degree of sophistication and flexibility to this procedure. AI language models can be tasked to identify novel perspectives within a large body of text through prompting alone. You simply need to tell the AI to find them.

In the absence of that ability to extract distinctive comments, lawmakers and regulators have no choice but to prioritize on other factors. If there is nothing better, “who donated the most to our campaign” or “which company employs the most of my former staffers” become reasonable metrics for prioritizing public comments. AI can help elected representatives do much better.

If Americans want AI to help revitalize the country’s ailing democracy, they need to think about how to align the incentives of elected leaders with those of individuals. Right now, as much as 90% of constituent communications are mass emails organized by advocacy groups, and they go largely ignored by staffers. People are channeling their passions into a vast digital warehouses where algorithms box up their expressions so they don’t have to be read. As a result, the incentive for citizens and advocacy groups is to fill that box up to the brim, so someone will notice it’s overflowing.

A talented, knowledgeable, engaged citizen should be able to articulate their ideas and share their personal experiences and distinctive points of view in a way that they can be both included with everyone else’s comments where they contribute to summarization and recognized individually among the other comments. An effective comment summarization process would extricate those unique points of view from the pile and put them into lawmakers’ hands.

This essay was written with Nathan Sanders, and previously appeared in the Conversation.

Indiana, Iowa, and Tennessee Pass Comprehensive Privacy Laws

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2023/05/indiana-iowa-and-tennessee-pass-comprehensive-privacy-laws.html

It’s been a big month for US data privacy. Indiana, Iowa, and Tennessee all passed state privacy laws, bringing the total number of states with a privacy law up to eight. No private right of action in any of those, which means it’s up to the states to enforce the laws.

UK Threatens End-to-End Encryption

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2023/04/uk-threatens-end-to-end-encryption.html

In an open letter, seven secure messaging apps—including Signal and WhatsApp—point out that the UK’s Online Safety Bill could destroy end-to-end encryption:

As currently drafted, the Bill could break end-to-end encryption,opening the door to routine, general and indiscriminate surveillance of personal messages of friends, family members, employees, executives, journalists, human rights activists and even politicians themselves, which would fundamentally undermine everyone’s ability to communicate securely.

The Bill provides no explicit protection for encryption, and if implemented as written, could empower OFCOM to try to force the proactive scanning of private messages on end-to-end encrypted communication services—nullifying the purpose of end-to-end encryption as a result and compromising the privacy of all users.

In short, the Bill poses an unprecedented threat to the privacy, safety and security of every UK citizen and the people with whom they communicate around the world, while emboldening hostile governments who may seek to draft copy-cat laws.

Both Signal and WhatsApp have said that they will cease services in the UK rather than compromise the security of their users worldwide.

EFF on the UN Cybercrime Treaty

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2023/04/eff-on-the-un-cybercrime-treaty.html

EFF has a good explainer on the problems with the new UN Cybercrime Treaty, currently being negotiated in Vienna.

The draft treaty has the potential to rewrite criminal laws around the world, possibly adding over 30 criminal offenses and new expansive police powers for both domestic and international criminal investigations.

[…]

While we don’t think the U.N. Cybercrime Treaty is necessary, we’ve been closely scrutinizing the process and providing constructive analysis. We’ve made clear that human rights must be baked into the proposed treaty so that it doesn’t become a tool to stifle freedom of expression, infringe on privacy and data protection, or endanger vulnerable people and communities.

Hacking Suicide

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2023/04/hacking-suicide.html

Here’s a religious hack:

You want to commit suicide, but it’s a mortal sin: your soul goes straight to hell, forever. So what you do is murder someone. That will get you executed, but if you confess your sins to a priest beforehand you avoid hell. Problem solved.

This was actually a problem in the 17th and 18th centuries in Northern Europe, particularly Denmark. And it remained a problem until capital punishment was abolished for murder.

It’s a clever hack. I didn’t learn about it in time to put it in my book, A Hacker’s Mind, but I have several other good hacks of religious rules.

How AI Could Write Our Laws

Post Syndicated from Schneier.com Webmaster original https://www.schneier.com/blog/archives/2023/03/how-ai-could-write-our-laws.html

Nearly 90% of the multibillion-dollar federal lobbying apparatus in the United States serves corporate interests. In some cases, the objective of that money is obvious. Google pours millions into lobbying on bills related to antitrust regulation. Big energy companies expect action whenever there is a move to end drilling leases for federal lands, in exchange for the tens of millions they contribute to congressional reelection campaigns.

But lobbying strategies are not always so blunt, and the interests involved are not always so obvious. Consider, for example, a 2013 Massachusetts bill that tried to restrict the commercial use of data collected from K-12 students using services accessed via the internet. The bill appealed to many privacy-conscious education advocates, and appropriately so. But behind the justification of protecting students lay a market-altering policy: the bill was introduced at the behest of Microsoft lobbyists, in an effort to exclude Google Docs from classrooms.

What would happen if such legal-but-sneaky strategies for tilting the rules in favor of one group over another become more widespread and effective? We can see hints of an answer in the remarkable pace at which artificial-intelligence tools for everything from writing to graphic design are being developed and improved. And the unavoidable conclusion is that AI will make lobbying more guileful, and perhaps more successful.

It turns out there is a natural opening for this technology: microlegislation.

“Microlegislation” is a term for small pieces of proposed law that cater—sometimes unexpectedly—to narrow interests. Political scientist Amy McKay coined the term. She studied the 564 amendments to the Affordable Care Act (“Obamacare”) considered by the Senate Finance Committee in 2009, as well as the positions of 866 lobbying groups and their campaign contributions. She documented instances where lobbyist comments—on health-care research, vaccine services, and other provisions—were translated directly into microlegislation in the form of amendments. And she found that those groups’ financial contributions to specific senators on the committee increased the amendments’ chances of passing.

Her finding that lobbying works was no surprise. More important, McKay’s work demonstrated that computer models can predict the likely fate of proposed legislative amendments, as well as the paths by which lobbyists can most effectively secure their desired outcomes. And that turns out to be a critical piece of creating an AI lobbyist.

Lobbying has long been part of the give-and-take among human policymakers and advocates working to balance their competing interests. The danger of microlegislation—a danger greatly exacerbated by AI—is that it can be used in a way that makes it difficult to figure out who the legislation truly benefits.

Another word for a strategy like this is a “hack.” Hacks follow the rules of a system but subvert their intent. Hacking is often associated with computer systems, but the concept is also applicable to social systems like financial markets, tax codes, and legislative processes.

While the idea of monied interests incorporating AI assistive technologies into their lobbying remains hypothetical, specific machine-learning technologies exist today that would enable them to do so. We should expect these techniques to get better and their utilization to grow, just as we’ve seen in so many other domains.

Here’s how it might work.

Crafting an AI microlegislator

To make microlegislation, machine-learning systems must be able to uncover the smallest modification that could be made to a bill or existing law that would make the biggest impact on a narrow interest.

There are three basic challenges involved. First, you must create a policy proposal—small suggested changes to legal text—and anticipate whether or not a human reader would recognize the alteration as substantive. This is important; a change that isn’t detectable is more likely to pass without controversy. Second, you need to do an impact assessment to project the implications of that change for the short- or long-range financial interests of companies. Third, you need a lobbying strategizer to identify what levers of power to pull to get the best proposal into law.

Existing AI tools can tackle all three of these.

The first step, the policy proposal, leverages the core function of generative AI. Large language models, the sort that have been used for general-purpose chatbots such as ChatGPT, can easily be adapted to write like a native in different specialized domains after seeing a relatively small number of examples. This process is called fine-tuning. For example, a model “pre-trained” on a large library of generic text samples from books and the internet can be “fine-tuned” to work effectively on medical literature, computer science papers, and product reviews.

Given this flexibility and capacity for adaptation, a large language model could be fine-tuned to produce draft legislative texts, given a data set of previously offered amendments and the bills they were associated with. Training data is available. At the federal level, it’s provided by the US Government Publishing Office, and there are already tools for downloading and interacting with it. Most other jurisdictions provide similar data feeds, and there are even convenient assemblages of that data.

Meanwhile, large language models like the one underlying ChatGPT are routinely used for summarizing long, complex documents (even laws and computer code) to capture the essential points, and they are optimized to match human expectations. This capability could allow an AI assistant to automatically predict how detectable the true effect of a policy insertion may be to a human reader.

Today, it can take a highly paid team of human lobbyists days or weeks to generate and analyze alternative pieces of microlegislation on behalf of a client. With AI assistance, that could be done instantaneously and cheaply. This opens the door to dramatic increases in the scope of this kind of microlegislating, with a potential to scale across any number of bills in any jurisdiction.

Teaching machines to assess impact

Impact assessment is more complicated. There is a rich series of methods for quantifying the predicted outcome of a decision or policy, and then also optimizing the return under that model. This kind of approach goes by different names in different circles—mathematical programming in management science, utility maximization in economics, and rational design in the life sciences.

To train an AI to do this, we would need to specify some way to calculate the benefit to different parties as a result of a policy choice. That could mean estimating the financial return to different companies under a few different scenarios of taxation or regulation. Economists are skilled at building risk models like this, and companies are already required to formulate and disclose regulatory compliance risk factors to investors. Such a mathematical model could translate directly into a reward function, a grading system that could provide feedback for the model used to create policy proposals and direct the process of training it.

The real challenge in impact assessment for generative AI models would be to parse the textual output of a model like ChatGPT in terms that an economic model could readily use. Automating this would require extracting structured financial information from the draft amendment or any legalese surrounding it. This kind of information extraction, too, is an area where AI has a long history; for example, AI systems have been trained to recognize clinical details in doctors’ notes. Early indications are that large language models are fairly good at recognizing financial information in texts such as investor call transcripts. While it remains an open challenge in the field, they may even be capable of writing out multi-step plans based on descriptions in free text.

Machines as strategists

The last piece of the puzzle is a lobbying strategizer to figure out what actions to take to convince lawmakers to adopt the amendment.

Passing legislation requires a keen understanding of the complex interrelated networks of legislative offices, outside groups, executive agencies, and other stakeholders vying to serve their own interests. Each actor in this network has a baseline perspective and different factors that influence that point of view. For example, a legislator may be moved by seeing an allied stakeholder take a firm position, or by a negative news story, or by a campaign contribution.

It turns out that AI developers are very experienced at modeling these kinds of networks. Machine-learning models for network graphs have been built, refined, improved, and iterated by hundreds of researchers working on incredibly diverse problems: lidar scans used to guide self-driving cars, the chemical functions of molecular structures, the capture of motion in actors’ joints for computer graphics, behaviors in social networks, and more.

In the context of AI-assisted lobbying, political actors like legislators and lobbyists are nodes on a graph, just like users in a social network. Relations between them are graph edges, like social connections. Information can be passed along those edges, like messages sent to a friend or campaign contributions made to a member. AI models can use past examples to learn to estimate how that information changes the network. Calculating the likelihood that a campaign contribution of a given size will flip a legislator’s vote on an amendment is one application.

McKay’s work has already shown us that there are significant, predictable relationships between these actions and the outcomes of legislation, and that the work of discovering those can be automated. Others have shown that graphs of neural network models like those described above can be applied to political systems. The full-scale use of these technologies to guide lobbying strategy is theoretical, but plausible.

Put together, these three components could create an automatic system for generating profitable microlegislation. The policy proposal system would create millions, even billions, of possible amendments. The impact assessor would identify the few that promise to be most profitable to the client. And the lobbying strategy tool would produce a blueprint for getting them passed.

What remains is for human lobbyists to walk the floors of the Capitol or state house, and perhaps supply some cash to grease the wheels. These final two aspects of lobbying—access and financing—cannot be supplied by the AI tools we envision. This suggests that lobbying will continue to primarily benefit those who are already influential and wealthy, and AI assistance will amplify their existing advantages.

The transformative benefit that AI offers to lobbyists and their clients is scale. While individual lobbyists tend to focus on the federal level or a single state, with AI assistance they could more easily infiltrate a large number of state-level (or even local-level) law-making bodies and elections. At that level, where the average cost of a seat is measured in the tens of thousands of dollars instead of millions, a single donor can wield a lot of influence—if automation makes it possible to coordinate lobbying across districts.

How to stop them

When it comes to combating the potentially adverse effects of assistive AI, the first response always seems to be to try to detect whether or not content was AI-generated. We could imagine a defensive AI that detects anomalous lobbyist spending associated with amendments that benefit the contributing group. But by then, the damage might already be done.

In general, methods for detecting the work of AI tend not to keep pace with its ability to generate convincing content. And these strategies won’t be implemented by AIs alone. The lobbyists will still be humans who take the results of an AI microlegislator and further refine the computer’s strategies. These hybrid human-AI systems will not be detectable from their output.

But the good news is: the same strategies that have long been used to combat misbehavior by human lobbyists can still be effective when those lobbyists get an AI assist. We don’t need to reinvent our democracy to stave off the worst risks of AI; we just need to more fully implement long-standing ideals.

First, we should reduce the dependence of legislatures on monolithic, multi-thousand-page omnibus bills voted on under deadline. This style of legislating exploded in the 1980s and 1990s and continues through to the most recent federal budget bill. Notwithstanding their legitimate benefits to the political system, omnibus bills present an obvious and proven vehicle for inserting unnoticed provisions that may later surprise the same legislators who approved them.

The issue is not that individual legislators need more time to read and understand each bill (that isn’t realistic or even necessary). It’s that omnibus bills must pass. There is an imperative to pass a federal budget bill, and so the capacity to push back on individual provisions that may seem deleterious (or just impertinent) to any particular group is small. Bills that are too big to fail are ripe for hacking by microlegislation.

Moreover, the incentive for legislators to introduce microlegislation catering to a narrow interest is greater if the threat of exposure is lower. To strengthen the threat of exposure for misbehaving legislative sponsors, bills should focus more tightly on individual substantive areas and, after the introduction of amendments, allow more time before the committee and floor votes. During this time, we should encourage public review and testimony to provide greater oversight.

Second, we should strengthen disclosure requirements on lobbyists, whether they’re entirely human or AI-assisted. State laws regarding lobbying disclosure are a hodgepodge. North Dakota, for example, only requires lobbying reports to be filed annually, so that by the time a disclosure is made, the policy is likely already decided. A lobbying disclosure scorecard created by Open Secrets, a group researching the influence of money in US politics, tracks nine states that do not even require lobbyists to report their compensation.

Ideally, it would be great for the public to see all communication between lobbyists and legislators, whether it takes the form of a proposed amendment or not. Absent that, let’s give the public the benefit of reviewing what lobbyists are lobbying for—and why. Lobbying is traditionally an activity that happens behind closed doors. Right now, many states reinforce that: they actually exempt testimony delivered publicly to a legislature from being reported as lobbying.

In those jurisdictions, if you reveal your position to the public, you’re no longer lobbying. Let’s do the inverse: require lobbyists to reveal their positions on issues. Some jurisdictions already require a statement of position (a ‘yea’ or ‘nay’) from registered lobbyists. And in most (but not all) states, you could make a public records request regarding meetings held with a state legislator and hope to get something substantive back. But we can expect more—lobbyists could be required to proactively publish, within a few days, a brief summary of what they demanded of policymakers during meetings and why they believe it’s in the general interest.

We can’t rely on corporations to be forthcoming and wholly honest about the reasons behind their lobbying positions. But having them on the record about their intentions would at least provide a baseline for accountability.

Finally, consider the role AI assistive technologies may have on lobbying firms themselves and the labor market for lobbyists. Many observers are rightfully concerned about the possibility of AI replacing or devaluing the human labor it automates. If the automating potential of AI ends up commodifying the work of political strategizing and message development, it may indeed put some professionals on K Street out of work.

But don’t expect that to disrupt the careers of the most astronomically compensated lobbyists: former members Congress and other insiders who have passed through the revolving door. There is no shortage of reform ideas for limiting the ability of government officials turned lobbyists to sell access to their colleagues still in government, and they should be adopted and—equally important—maintained and enforced in successive Congresses and administrations.

None of these solutions are really original, specific to the threats posed by AI, or even predominantly focused on microlegislation—and that’s the point. Good governance should and can be robust to threats from a variety of techniques and actors.

But what makes the risks posed by AI especially pressing now is how fast the field is developing. We expect the scale, strategies, and effectiveness of humans engaged in lobbying to evolve over years and decades. Advancements in AI, meanwhile, seem to be making impressive breakthroughs at a much faster pace—and it’s still accelerating.

The legislative process is a constant struggle between parties trying to control the rules of our society as they are updated, rewritten, and expanded at the federal, state, and local levels. Lobbying is an important tool for balancing various interests through our system. If it’s well-regulated, perhaps lobbying can support policymakers in making equitable decisions on behalf of us all.

This article was co-written with Nathan E. Sanders and originally appeared in MIT Technology Review.

Nick Weaver on Regulating Cryptocurrency

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2023/03/nick-weaver-on-regulating-cryptocurrency.html

Nicholas Weaver wrote an excellent paper on the problems of cryptocurrencies and the need to regulate the space—with all existing regulations. His conclusion:

Regulators, especially regulators in the United States, often fear accusations of stifling innovation. As such, the cryptocurrency space has grown over the past decade with very little regulatory oversight.

But fortunately for regulators, there is no actual innovation to stifle. Cryptocurrencies cannot revolutionize payments or finance, as the basic nature of all cryptocurrencies render them fundamentally unsuitable to revolutionize our financial system—which, by the way, already has decades of successful experience with digital payments and electronic money. The supposedly “decentralized” and “trustless” cryptocurrency systems, both technically and socially, fail to provide meaningful benefits to society—and indeed, necessarily also fail in their foundational claims of decentralization and trustlessness.

When regulating cryptocurrencies, the best starting point is history. Regulating various tokens is best done through the existing securities law framework, an area where the US has a near century of well-established law. It starts with regulating the issuance of new cryptocurrency tokens and related securities. This should substantially reduce the number of fraudulent offerings.

Similarly, active regulation of the cryptocurrency exchanges should offer substantial benefits, including eliminating significant consumer risk, blocking key money-laundering channels, and overall producing a far more regulated and far less manipulated market.

Finally, the stablecoins need basic regulation as money transmitters. Unless action is taken they risk becoming substantial conduits for money laundering, but requiring them to treat all users as customers should prevent this risk from developing further.

Read the whole thing.

Defending against AI Lobbyists

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2023/02/defending-against-ai-lobbyists.html

When is it time to start worrying about artificial intelligence interfering in our democracy? Maybe when an AI writes a letter to The New York Times opposing the regulation of its own technology.

That happened last month. And because the letter was responding to an essay we wrote, we’re starting to get worried. And while the technology can be regulated, the real solution lies in recognizing that the problem is human actors—and those we can do something about.

Our essay argued that the much heralded launch of the AI chatbot ChatGPT, a system that can generate text realistic enough to appear to be written by a human, poses significant threats to democratic processes. The ability to produce high quality political messaging quickly and at scale, if combined with AI-assisted capabilities to strategically target those messages to policymakers and the public, could become a powerful accelerant of an already sprawling and poorly constrained force in modern democratic life: lobbying.

We speculated that AI-assisted lobbyists could use generative models to write op-eds and regulatory comments supporting a position, identify members of Congress who wield the most influence over pending legislation, use network pattern identification to discover undisclosed or illegal political coordination, or use supervised machine learning to calibrate the optimal contribution needed to sway the vote of a legislative committee member.

These are all examples of what we call AI hacking. Hacks are strategies that follow the rules of a system, but subvert its intent. Currently a human creative process, future AIs could discover, develop, and execute these same strategies.

While some of these activities are the longtime domain of human lobbyists, AI tools applied against the same task would have unfair advantages. They can scale their activity effortlessly across every state in the country—human lobbyists tend to focus on a single state—they may uncover patterns and approaches unintuitive and unrecognizable by human experts, and do so nearly instantaneously with little chance for human decision makers to keep up.

These factors could make AI hacking of the democratic process fundamentally ungovernable. Any policy response to limit the impact of AI hacking on political systems would be critically vulnerable to subversion or control by an AI hacker. If AI hackers achieve unchecked influence over legislative processes, they could dictate the rules of our society: including the rules that govern AI.

We admit that this seemed far fetched when we first wrote about it in 2021. But now that the emanations and policy prescriptions of ChatGPT have been given an audience in the New York Times and innumerable other outlets in recent weeks, it’s getting harder to dismiss.

At least one group of researchers is already testing AI techniques to automatically find and advocate for bills that benefit a particular interest. And one Massachusetts representative used ChatGPT to draft legislation regulating AI.

The AI technology of two years ago seems quaint by the standards of ChatGPT. What will the technology of 2025 seem like if we could glimpse it today? To us there is no question that now is the time to act.

First, let’s dispense with the concepts that won’t work. We cannot solely rely on explicit regulation of AI technology development, distribution, or use. Regulation is essential, but it would be vastly insufficient. The rate of AI technology development, and the speed at which AI hackers might discover damaging strategies, already outpaces policy development, enactment, and enforcement.

Moreover, we cannot rely on detection of AI actors. The latest research suggests that AI models trying to classify text samples as human- or AI-generated have limited precision, and are ill equipped to handle real world scenarios. These reactive, defensive techniques will fail because the rate of advancement of the “offensive” generative AI is so astounding.

Additionally, we risk a dragnet that will exclude masses of human constituents that will use AI to help them express their thoughts, or machine translation tools to help them communicate. If a written opinion or strategy conforms to the intent of a real person, it should not matter if they enlisted the help of an AI (or a human assistant) to write it.

Most importantly, we should avoid the classic trap of societies wrenched by the rapid pace of change: privileging the status quo. Slowing down may seem like the natural response to a threat whose primary attribute is speed. Ideas like increasing requirements for human identity verification, aggressive detection regimes for AI-generated messages, and elongation of the legislative or regulatory process would all play into this fallacy. While each of these solutions may have some value independently, they do nothing to make the already powerful actors less powerful.

Finally, it won’t work to try to starve the beast. Large language models like ChatGPT have a voracious appetite for data. They are trained on past examples of the kinds of content that they will be asked to generate in the future. Similarly, an AI system built to hack political systems will rely on data that documents the workings of those systems, such as messages between constituents and legislators, floor speeches, chamber and committee voting results, contribution records, lobbying relationship disclosures, and drafts of and amendments to legislative text. The steady advancement towards the digitization and publication of this information that many jurisdictions have made is positive. The threat of AI hacking should not dampen or slow progress on transparency in public policymaking.

Okay, so what will help?

First, recognize that the true threats here are malicious human actors. Systems like ChatGPT and our still-hypothetical political-strategy AI are still far from artificial general intelligences. They do not think. They do not have free will. They are just tools directed by people, much like lobbyist for hire. And, like lobbyists, they will be available primarily to the richest individuals, groups, and their interests.

However, we can use the same tools that would be effective in controlling human political influence to curb AI hackers. These tools will be familiar to any follower of the last few decades of U.S. political history.

Campaign finance reforms such as contribution limits, particularly when applied to political action committees of all types as well as to candidate operated campaigns, can reduce the dependence of politicians on contributions from private interests. The unfair advantage of a malicious actor using AI lobbying tools is at least somewhat mitigated if a political target’s entire career is not already focused on cultivating a concentrated set of major donors.

Transparency also helps. We can expand mandatory disclosure of contributions and lobbying relationships, with provisions to prevent the obfuscation of the funding source. Self-interested advocacy should be transparently reported whether or not it was AI-assisted. Meanwhile, we should increase penalties for organizations that benefit from AI-assisted impersonation of constituents in political processes, and set a greater expectation of responsibility to avoid “unknowing” use of these tools on their behalf.

Our most important recommendation is less legal and more cultural. Rather than trying to make it harder for AI to participate in the political process, make it easier for humans to do so.

The best way to fight an AI that can lobby for moneyed interests is to help the little guy lobby for theirs. Promote inclusion and engagement in the political process so that organic constituent communications grow alongside the potential growth of AI-directed communications. Encourage direct contact that generates more-than-digital relationships between constituents and their representatives, which will be an enduring way to privilege human stakeholders. Provide paid leave to allow people to vote as well as to testify before their legislature and participate in local town meetings and other civic functions. Provide childcare and accessible facilities at civic functions so that more community members can participate.

The threat of AI hacking our democracy is legitimate and concerning, but its solutions are consistent with our democratic values. Many of the ideas above are good governance reforms already being pushed and fought over at the federal and state level.

We don’t need to reinvent our democracy to save it from AI. We just need to continue the work of building a just and equitable political system. Hopefully ChatGPT will give us all some impetus to do that work faster.

This essay was written with Nathan Sanders, and appeared on the Belfer Center blog.

What Will It Take?

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2023/02/what-will-it-take.html

What will it take for policy makers to take cybersecurity seriously? Not minimal-change seriously. Not here-and-there seriously. But really seriously. What will it take for policy makers to take cybersecurity seriously enough to enact substantive legislative changes that would address the problems? It’s not enough for the average person to be afraid of cyberattacks. They need to know that there are engineering fixes—and that’s something we can provide.

For decades, I have been waiting for the “big enough” incident that would finally do it. In 2015, Chinese military hackers hacked the Office of Personal Management and made off with the highly personal information of about 22 million Americans who had security clearances. In 2016, the Mirai botnet leveraged millions of Internet-of-Things devices with default admin passwords to launch a denial-of-service attack that disabled major Internet platforms and services in both North America and Europe. In 2017, hackers—years later we learned that it was the Chinese military—hacked the credit bureau Equifax and stole the personal information of 147 million Americans. In recent years, ransomware attacks have knocked hospitals offline, and many articles have been written about Russia inside the U.S. power grid. And last year, the Russian SVR hacked thousands of sensitive networks inside civilian critical infrastructure worldwide in what we’re now calling Sunburst (and used to call SolarWinds).

Those are all major incidents to security people, but think about them from the perspective of the average person. Even the most spectacular failures don’t affect 99.9% of the country. Why should anyone care if the Chinese have his or her credit records? Or if the Russians are stealing data from some government network? Few of us have been directly affected by ransomware, and a temporary Internet outage is just temporary.

Cybersecurity has never been a campaign issue. It isn’t a topic that shows up in political debates. (There was one question in a 2016 Clinton–Trump debate, but the response was predictably unsubstantive.) This just isn’t an issue that most people prioritize, or even have an opinion on.

So, what will it take? Many of my colleagues believe that it will have to be something with extreme emotional intensity—sensational, vivid, salient—that results in large-scale loss of life or property damage. A successful attack that actually poisons a water supply, as someone tried to do in January by raising the levels of lye at a Florida water-treatment plant. (That one was caught early.) Or an attack that disables Internet-connected cars at speed, something that was demonstrated by researchers in 2014. Or an attack on the power grid, similar to what Russia did to the Ukraine in 2015 and 2016. Will it take gas tanks exploding and planes falling out of the sky for the average person to read about the casualties and think “that could have been me”?

Here’s the real problem. For the average nonexpert—and in this category I include every lawmaker—to push for change, they not only need to believe that the present situation is intolerable, they also need to believe that an alternative is possible. Real legislative change requires a belief that the never-ending stream of hacks and attacks is not inevitable, that we can do better. And that will require creating working examples of secure, dependable, resilient systems.

Providing alternatives is how engineers help facilitate social change. We could never have eliminated sales of tungsten-filament household light bulbs if fluorescent and LED replacements hadn’t become available. Reducing the use of fossil fuel for electricity generation requires working wind turbines and cost-effective solar cells.

We need to demonstrate that it’s possible to build systems that can defend themselves against hackers, criminals, and national intelligence agencies; secure Internet-of-Things systems; and systems that can reestablish security after a breach. We need to prove that hacks aren’t inevitable, and that our vulnerability is a choice. Only then can someone decide to choose differently. When people die in a cyberattack and everyone asks “What can be done?” we need to have something to tell them.

We don’t yet have the technology to build a truly safe, secure, and resilient Internet and the computers that connect to it. Yes, we have lots of security technologies. We have older secure systems—anyone still remember Apollo’s DomainOS and MULTICS?—that lost out in a market that didn’t reward security. We have newer research ideas and products that aren’t successful because the market still doesn’t reward security. We have even newer research ideas that won’t be deployed, again, because the market still prefers convenience over security.

What I am proposing is something more holistic, an engineering research task on a par with the Internet itself. The Internet was designed and built to answer this question: Can we build a reliable network out of unreliable parts in an unreliable world? It turned out the answer was yes, and the Internet was the result. I am asking a similar research question: Can we build a secure network out of insecure parts in an insecure world? The answer isn’t obviously yes, but it isn’t obviously no, either.

While any successful demonstration will include many of the security technologies we know and wish would see wider use, it’s much more than that. Creating a secure Internet ecosystem goes beyond old-school engineering to encompass the social sciences. It will include significant economic, institutional, and psychological considerations that just weren’t present in the first few decades of Internet research.

Cybersecurity isn’t going to get better until the economic incentives change, and that’s not going to change until the political incentives change. The political incentives won’t change until there is political liability that comes from voter demands. Those demands aren’t going to be solely the results of insecurity. They will also be the result of believing that there’s a better alternative. It is our task to research, design, build, test, and field that better alternative—even though the market couldn’t care less right now.

This essay originally appeared in the May/June 2021 issue of IEEE Security & Privacy. I forgot to publish it here.

Hacking the Tax Code

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2023/02/hacking-the-tax-code.html

The tax code isn’t software. It doesn’t run on a computer. But it’s still code. It’s a series of algorithms that takes an input—financial information for the year—and produces an output: the amount of tax owed. It’s incredibly complex code; there are a bazillion details and exceptions and special cases. It consists of government laws, rulings from the tax authorities, judicial decisions, and legal opinions.

Like computer code, the tax code has bugs. They might be mistakes in how the tax laws were written. They might be mistakes in how the tax code is interpreted, oversights in how parts of the law were conceived, or unintended omissions of some sort or another. They might arise from the exponentially huge number of ways different parts of the tax code interact.

A recent example comes from the 2017 Tax Cuts and Jobs Act. That law was drafted in both haste and secret, and quickly passed without any time for review—or even proofreading. One of the things in it was a typo that accidentally categorized military death benefits as earned income. The practical effect of that mistake is that surviving family members were hit with surprise tax bills of US$10,000 or more.

That’s a bug, but not a vulnerability. An example of a vulnerability is the “Double Irish with a Dutch Sandwich.” It arises from the interactions of tax laws in multiple countries, and it’s how companies like Google and Apple have avoided paying U.S. taxes despite being U.S. companies. Estimates are that U.S. companies avoided paying nearly US$200 billion in taxes in 2017 alone.

In the tax world, vulnerabilities are called loopholes. Exploits are called tax avoidance strategies. And there are thousands of black-hat researchers who examine every line of the tax code looking for exploitable vulnerabilities—tax attorneys and tax accountants.

Some vulnerabilities are deliberately created. Lobbyists are constantly trying to insert this or that provision into the tax code that benefits their clients financially. That same 2017 U.S. tax law included a special tax break for oil and gas investment partnerships, a special exemption that ensures that fewer than 1 in 1,000 estates will have to pay estate tax, and language specifically expanding a pass-through loophole that industry uses to incorporate companies offshore and avoid U.S. taxes. That’s not hacking the tax code. It’s hacking the processes that create them: the legislative process that creates tax law.

We know the processes to use to fix vulnerabilities in computer code. Before the code is finished, we can employ some sort of secure development processes, with automatic bug-finding tools and maybe source code audits. After the code is deployed, we might rely on vulnerability finding by the security community, perhaps bug bounties—and most of all, quick patching when vulnerabilities are discovered.

What does it mean to “patch” the tax code? Passing any tax legislation is a big deal, especially in the United States where the issue is so partisan and contentious. (That 2017 earned income tax bug for military families hasn’t yet been fixed. And that’s an easy one; everyone acknowledges it was a mistake.) We don’t have the ability to patch tax code with anywhere near the same agility that we have to patch software.

We can patch some vulnerabilities, though. The other way tax code is modified is by IRS and judicial rulings. The 2017 tax law capped income tax deductions for property taxes. This provision didn’t come into force in 2018, so someone came up with the clever hack to prepay 2018 property taxes in 2017. Just before the end of the year, the IRS ruled about when that was legal and when it wasn’t. Short answer: most of the time, it wasn’t.

There’s another option: that the vulnerability isn’t patched and isn’t explicitly approved, and slowly becomes part of the normal way of doing things. Lots of tax loopholes end up like this. Sometimes they’re even given retroactive legality by the IRS or Congress after a constituency and lobbying effort gets behind them. This process is how systems evolve. A hack subverts the intent of a system. Whatever governing system has jurisdiction either blocks the hack or allows it—or does nothing and the hack becomes the new normal.

Here’s my question: what happens when artificial intelligence and machine learning (ML) gets hold of this problem? We already have ML systems that find software vulnerabilities. What happens when you feed a ML system the entire U.S. tax code and tell it to figure out all of the ways to minimize the amount of tax owed? Or, in the case of a multinational corporation, to feed it the entire planet’s tax codes? What sort of vulnerabilities would it find? And how many? Dozens or millions?

In 2015, Volkswagen was caught cheating on emissions control tests. It didn’t forge test results; it got the cars’ computers to cheat for them. Engineers programmed the software in the car’s onboard computer to detect when the car was undergoing an emissions test. The computer then activated the car’s emissions-curbing systems, but only for the duration of the test. The result was that the cars had much better performance on the road at the cost of producing more pollution.

ML will result in lots of hacks like this. They’ll be more subtle. They’ll be even harder to discover. It’s because of the way ML systems optimize themselves, and because their specific optimizations can be impossible for us humans to understand. Their human programmers won’t even know what’s going on.

Any good ML system will naturally find and exploit hacks. This is because their only constraints are the rules of the system. If there are problems, inconsistencies, or loopholes in the rules, and if those properties lead to a “better” solution as defined by the program, then those systems will find them. The challenge is that you have to define the system’s goals completely and precisely, and that that’s impossible.

The tax code can be hacked. Financial markets regulations can be hacked. The market economy, democracy itself, and our cognitive systems can all be hacked. Tasking a ML system to find new hacks against any of these is still science fiction, but it’s not stupid science fiction. And ML will drastically change how we need to think about policy, law, and government. Now’s the time to figure out how.

This essay originally appeared in the September/October 2020 issue of IEEE Security & Privacy. I wrote it when I started writing my latest book, but never published it here.

AI and Political Lobbying

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2023/01/ai-and-political-lobbying.html

Launched just weeks ago, ChatGPT is already threatening to upend how we draft everyday communications like emails, college essays and myriad other forms of writing.

Created by the company OpenAI, ChatGPT is a chatbot that can automatically respond to written prompts in a manner that is sometimes eerily close to human.

But for all the consternation over the potential for humans to be replaced by machines in formats like poetry and sitcom scripts, a far greater threat looms: artificial intelligence replacing humans in the democratic processes—not through voting, but through lobbying.

ChatGPT could automatically compose comments submitted in regulatory processes. It could write letters to the editor for publication in local newspapers. It could comment on news articles, blog entries and social media posts millions of times every day. It could mimic the work that the Russian Internet Research Agency did in its attempt to influence our 2016 elections, but without the agency’s reported multimillion-dollar budget and hundreds of employees.

Automatically generated comments aren’t a new problem. For some time, we have struggled with bots, machines that automatically post content. Five years ago, at least a million automatically drafted comments were believed to have been submitted to the Federal Communications Commission regarding proposed regulations on net neutrality. In 2019, a Harvard undergraduate, as a test, used a text-generation program to submit 1,001 comments in response to a government request for public input on a Medicaid issue. Back then, submitting comments was just a game of overwhelming numbers.

Platforms have gotten better at removing “coordinated inauthentic behavior.” Facebook, for example, has been removing over a billion fake accounts a year. But such messages are just the beginning. Rather than flooding legislators’ inboxes with supportive emails, or dominating the Capitol switchboard with synthetic voice calls, an AI system with the sophistication of ChatGPT but trained on relevant data could selectively target key legislators and influencers to identify the weakest points in the policymaking system and ruthlessly exploit them through direct communication, public relations campaigns, horse trading or other points of leverage.

When we humans do these things, we call it lobbying. Successful agents in this sphere pair precision message writing with smart targeting strategies. Right now, the only thing stopping a ChatGPT-equipped lobbyist from executing something resembling a rhetorical drone warfare campaign is a lack of precision targeting. AI could provide techniques for that as well.

A system that can understand political networks, if paired with the textual-generation capabilities of ChatGPT, could identify the member of Congress with the most leverage over a particular policy area—say, corporate taxation or military spending. Like human lobbyists, such a system could target undecided representatives sitting on committees controlling the policy of interest and then focus resources on members of the majority party when a bill moves toward a floor vote.

Once individuals and strategies are identified, an AI chatbot like ChatGPT could craft written messages to be used in letters, comments—anywhere text is useful. Human lobbyists could also target those individuals directly. It’s the combination that’s important: Editorial and social media comments only get you so far, and knowing which legislators to target isn’t itself enough.

This ability to understand and target actors within a network would create a tool for AI hacking, exploiting vulnerabilities in social, economic and political systems with incredible speed and scope. Legislative systems would be a particular target, because the motive for attacking policymaking systems is so strong, because the data for training such systems is so widely available and because the use of AI may be so hard to detect—particularly if it is being used strategically to guide human actors.

The data necessary to train such strategic targeting systems will only grow with time. Open societies generally make their democratic processes a matter of public record, and most legislators are eager—at least, performatively so—to accept and respond to messages that appear to be from their constituents.

Maybe an AI system could uncover which members of Congress have significant sway over leadership but still have low enough public profiles that there is only modest competition for their attention. It could then pinpoint the SuperPAC or public interest group with the greatest impact on that legislator’s public positions. Perhaps it could even calibrate the size of donation needed to influence that organization or direct targeted online advertisements carrying a strategic message to its members. For each policy end, the right audience; and for each audience, the right message at the right time.

What makes the threat of AI-powered lobbyists greater than the threat already posed by the high-priced lobbying firms on K Street is their potential for acceleration. Human lobbyists rely on decades of experience to find strategic solutions to achieve a policy outcome. That expertise is limited, and therefore expensive.

AI could, theoretically, do the same thing much more quickly and cheaply. Speed out of the gate is a huge advantage in an ecosystem in which public opinion and media narratives can become entrenched quickly, as is being nimble enough to shift rapidly in response to chaotic world events.

Moreover, the flexibility of AI could help achieve influence across many policies and jurisdictions simultaneously. Imagine an AI-assisted lobbying firm that can attempt to place legislation in every single bill moving in the US Congress, or even across all state legislatures. Lobbying firms tend to work within one state only, because there are such complex variations in law, procedure and political structure. With AI assistance in navigating these variations, it may become easier to exert power across political boundaries.

Just as teachers will have to change how they give students exams and essay assignments in light of ChatGPT, governments will have to change how they relate to lobbyists.

To be sure, there may also be benefits to this technology in the democracy space; the biggest one is accessibility. Not everyone can afford an experienced lobbyist, but a software interface to an AI system could be made available to anyone. If we’re lucky, maybe this kind of strategy-generating AI could revitalize the democratization of democracy by giving this kind of lobbying power to the powerless.

However, the biggest and most powerful institutions will likely use any AI lobbying techniques most successfully. After all, executing the best lobbying strategy still requires insiders—people who can walk the halls of the legislature—and money. Lobbying isn’t just about giving the right message to the right person at the right time; it’s also about giving money to the right person at the right time. And while an AI chatbot can identify who should be on the receiving end of those campaign contributions, humans will, for the foreseeable future, need to supply the cash. So while it’s impossible to predict what a future filled with AI lobbyists will look like, it will probably make the already influential and powerful even more so.

This essay was written with Nathan Sanders, and previously appeared in the New York Times.

Edited to Add: After writing this, we discovered that a research group is researching AI and lobbying:

We used autoregressive large language models (LLMs, the same type of model behind the now wildly popular ChatGPT) to systematically conduct the following steps. (The full code is available at this GitHub link: https://github.com/JohnNay/llm-lobbyist.)

  1. Summarize official U.S. Congressional bill summaries that are too long to fit into the context window of the LLM so the LLM can conduct steps 2 and 3.
  2. Using either the original official bill summary (if it was not too long), or the summarized version:
    1. Assess whether the bill may be relevant to a company based on a company’s description in its SEC 10K filing.
    2. Provide an explanation for why the bill is relevant or not.
    3. Provide a confidence level to the overall answer.
  3. If the bill is deemed relevant to the company by the LLM, draft a letter to the sponsor of the bill arguing for changes to the proposed legislation.

Here is the paper.

Decarbonizing Cryptocurrencies through Taxation

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2023/01/decarbonizing-cryptocurrencies-through-taxation.html

Maintaining bitcoin and other cryptocurrencies causes about 0.3 percent of global CO2 emissions. That may not sound like a lot, but it’s more than the emissions of Switzerland, Croatia, and Norway combined. As many cryptocurrencies crash and the FTX bankruptcy moves into the litigation stage, regulators are likely to scrutinize the cryptocurrency world more than ever before. This presents a perfect opportunity to curb their environmental damage.

The good news is that cryptocurrencies don’t have to be carbon intensive. In fact, some have near-zero emissions. To encourage polluting currencies to reduce their carbon footprint, we need to force buyers to pay for their environmental harms through taxes.

The difference in emissions among cryptocurrencies comes down to how they create new coins. Bitcoin and other high emitters use a system called “proof of work“: to generate coins, participants, or “miners,” have to solve math problems that demand extraordinary computing power. This allows currencies to maintain their decentralized ledger—the blockchain—but requires enormous amounts of energy.

Greener alternatives exist. Most notably, the “proof of stake” system enables participants to maintain their blockchain by depositing cryptocurrency holdings in a pool. When the second-largest cryptocurrency, Ethereum, switched from proof of work to proof of stake earlier this year, its energy consumption dropped by more than 99.9% overnight.

Bitcoin and other cryptocurrencies probably won’t follow suit unless forced to, because proof of work offers massive profits to miners—and they’re the ones with power in the system. Multiple legislative levers could be used to entice them to change.

The most blunt solution is to ban cryptocurrency mining altogether. China did this in 2018, but it only made the problem worse; mining moved to other countries with even less efficient energy generation, and emissions went up. The only way for a mining ban to meaningfully reduce carbon emissions is to enact it across most of the globe. Achieving that level of international consensus is, to say the least, unlikely.

A second solution is to prohibit the buying and selling of proof-of-work currencies. The European Parliament’s Committee on Economic and Monetary Affairs considered making such a proposal, but voted against it in March. This is understandable; as with a mining ban, it would be both viewed as paternalistic and difficult to implement politically.

Employing a tax instead of an outright ban would largely skirt these issues. As with taxes on gasoline, tobacco, plastics, and alcohol, a cryptocurrency tax could reduce real-world harm by making consumers pay for it.

Most ways of taxing cryptocurrencies would be inefficient, because they’re easy to circumvent and hard to enforce. To avoid these pitfalls, the tax should be levied as a fixed percentage of each proof-of-work-cryptocurrency purchase. Cryptocurrency exchanges should collect the tax, just as merchants collect sales taxes from customers before passing the sum on to governments. To make it harder to evade, the tax should apply regardless of how the proof-of-work currency is being exchanged—whether for a fiat currency or another cryptocurrency. Most important, any state that implements the tax should target all purchases by citizens in its jurisdiction, even if they buy through exchanges with no legal presence in the country.

This sort of tax would be transparent and easy to enforce. Because most people buy cryptocurrencies from one of only a few large exchanges—such as Binance, Coinbase, and Kraken—auditing them should be cheap enough that it pays for itself. If an exchange fails to comply, it should be banned.

Even a small tax on proof-of-work currencies would reduce their damage to the planet. Imagine that you’re new to cryptocurrency and want to become a first-time investor. You’re presented with a range of currencies to choose from: bitcoin, ether, litecoin, monero, and others. You notice that all of them except ether add an environmental tax to your purchase price. Which one do you buy?

Countries don’t need to coordinate across borders for a proof-of-work tax on their own citizens to be effective. But early adopters should still consider ways to encourage others to come on board. This has precedent. The European Union is trying to influence global policy with its carbon border adjustments, which are designed to discourage people from buying carbon-intensive products abroad in order to skirt taxes. Similar rules for a proof-of-work tax could persuade other countries to adopt one.

Of course, some people will try to evade the tax, just as people evade every other tax. For example, people might buy tax-free coins on centralized exchanges and then swap them for polluting coins on decentralized exchanges. To some extent, this is inevitable; no tax is perfect. But the effort and technical know-how needed to evade a proof-of-work tax will be a major deterrent.

Even if only a few countries implement this tax—and even if some people evade it—the desirability of bitcoin will fall globally, and the environmental benefit will be significant. A high enough tax could also cause a self-reinforcing cycle that will drive down these cryptocurrencies’ prices. Because the value of many cryptocurrencies rely largely on speculation, they are dependent on future buyers. When speculators are deterred by the tax, the lack of demand will cause the price of bitcoin to fall, which could prompt more current holders to sell—further lowering prices and accelerating the effect. Declining prices will pressure the bitcoin community to abandon proof of work altogether.

Taxing proof-of-work exchanges might hurt them in the short run, but it would not hinder blockchain innovation. Instead, it would redirect innovation toward greener cryptocurrencies. This is no different than how government incentives for electric vehicles encourage carmakers to improve green alternatives to the internal combustion engine. These incentives don’t restrict innovation in automobiles—they promote it.

Taxing environmentally harmful cryptocurrencies can gain support across the political spectrum, from people with varied interests. It would benefit blockchain innovators and cryptocurrency researchers by shifting focus from environmental harm to beneficial uses of the technology. It has the potential to make our planet significantly greener. It would increase government revenues.

Even bitcoin maximalists have reason to embrace the proposal: it would offer the bitcoin community a chance to prove it can survive and grow sustainably.

This essay was written with Christos Porios, and previously appeared in the Atlantic.

Hidden Anti-Cryptography Provisions in Internet Anti-Trust Bills

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2022/06/hidden-anti-cryptography-provisions-in-internet-anti-trust-bills.html

Two bills attempting to reduce the power of Internet monopolies are currently being debated in Congress: S. 2992, the American Innovation and Choice Online Act; and S. 2710, the Open App Markets Act. Reducing the power to tech monopolies would do more to “fix” the Internet than any other single action, and I am generally in favor of them both. (The Center for American Progress wrote a good summary and evaluation of them. I have written in support of the bill that would force Google and Apple to give up their monopolies on their phone app stores.)

There is a significant problem, though. Both bills have provisions that could be used to break end-to-end encryption.

Let’s start with S. 2992. Sec. 3(c)(7)(A)(iii) would allow a company to deny access to apps installed by users, where those app makers “have been identified [by the Federal Government] as national security, intelligence, or law enforcement risks.” That language is far too broad. It would allow Apple to deny access to an encryption service provider that provides encrypted cloud backups to the cloud (which Apple does not currently offer). All Apple would need to do is point to any number of FBI materials decrying the security risks with “warrant proof encryption.”

Sec. 3(c)(7)(A)(vi) states that there shall be no liability for a platform “solely” because it offers “end-to-end encryption.” This language is too narrow. The word “solely” suggests that offering end-to-end encryption could be a factor in determining liability, provided that it is not the only reason. This is very similar to one of the problems with the encryption carve-out in the EARN IT Act. The section also doesn’t mention any other important privacy-protective features and policies, which also shouldn’t be the basis for creating liability for a covered platform under Sec. 3(a).

In Sec. 2(a)(2), the definition of business user excludes any person who “is a clear national security risk.” This term is undefined, and as such far too broad. It can easily be interpreted to cover any company that offers an end-to-end encrypted alternative, or a service offered in a country whose privacy laws forbid disclosing data in response to US court-ordered surveillance. Again, the FBI’s repeated statements about end-to-end encryption could serve as support.

Finally, under Sec. 3(b)(2)(B), platforms have an affirmative defense for conduct that would otherwise violate the Act if they do so in order to “protect safety, user privacy, the security of nonpublic data, or the security of the covered platform.” This language is too vague, and could be used to deny users the ability to use competing services that offer better security/privacy than the incumbent platform—particularly where the platform offers subpar security in the name of “public safety.” For example, today Apple only offers unencrypted iCloud backups, which it can then turn over governments who claim this is necessary for “public safety.” Apple can raise this defense to justify its blocking third-party services from offering competing, end-to-end encrypted backups of iMessage and other sensitive data stored on an iPhone.

S. 2710 has similar problems. Sec 7. (6)(B) contains language specifying that the bill does not “require a covered company to interoperate or share data with persons or business users that…have been identified by the Federal Government as national security, intelligence, or law enforcement risks.” This would mean that Apple could ignore the prohibition against private APIs, and deny access to otherwise private APIs, for developers of encryption products that have been publicly identified by the FBI. That is, end-to-end encryption products.

I want those bills to pass, but I want those provisions cleared up so we don’t lose strong end-to-end encryption in our attempt to reign in the tech monopolies.

EDITED TO ADD (6/23): A few DC insiders have responded to me about this post. Their basic point is this: “Your threat model is wrong. The big tech companies can already break end-to-end encryption if they want. They don’t need any help, and this bill doesn’t give the FBI any new leverage they don’t already have. This bill doesn’t make anything any worse than it is today.” That’s a reasonable response. These bills are definitely a net positive for humanity.