Tag Archives: laws

Banning VPNs

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2025/12/banning-vpns.html

This is crazy. Lawmakers in several US states are contemplating banning VPNs, because…think of the children!

As of this writing, Wisconsin lawmakers are escalating their war on privacy by targeting VPNs in the name of “protecting children” in A.B. 105/S.B. 130. It’s an age verification bill that requires all websites distributing material that could conceivably be deemed “sexual content” to both implement an age verification system and also to block the access of users connected via VPN. The bill seeks to broadly expand the definition of materials that are “harmful to minors” beyond the type of speech that states can prohibit minors from accessing­ potentially encompassing things like depictions and discussions of human anatomy, sexuality, and reproduction.

The EFF link explains why this is a terrible idea.

On Hacking Back

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2025/11/on-hacking-back.html

Former DoJ attorney John Carlin writes about hackback, which he defines thus: “A hack back is a type of cyber response that incorporates a counterattack designed to proactively engage with, disable, or collect evidence about an attacker. Although hack backs can take on various forms, they are—­by definition­—not passive defensive measures.”

His conclusion:

As the law currently stands, specific forms of purely defense measures are authorized so long as they affect only the victim’s system or data.

At the other end of the spectrum, offensive measures that involve accessing or otherwise causing damage or loss to the hacker’s systems are likely prohibited, absent government oversight or authorization. And even then parties should proceed with caution in light of the heightened risks of misattribution, collateral damage, and retaliation.

As for the broad range of other hack back tactics that fall in the middle of active defense and offensive measures, private parties should continue to engage in these tactics only with government oversight or authorization. These measures exist within a legal gray area and would likely benefit from amendments to the CFAA and CISA that clarify and carve out the parameters of authorization for specific self-defense measures. But in the absence of amendments or clarification on the scope of those laws, private actors can seek governmental authorization through an array of channels, whether they be partnering with law enforcement or seeking authorization to engage in more offensive tactics from the courts in connection with private litigation.

The Semiconductor Industry and Regulatory Compliance

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2025/08/its-time-for-the-semiconductor-industry-to-step-up.html

Earlier this week, the Trump administration narrowed export controls on advanced semiconductors ahead of US-China trade negotiations. The administration is increasingly relying on export licenses to allow American semiconductor firms to sell their products to Chinese customers, while keeping the most powerful of them out of the hands of our military adversaries. These are the chips that power the artificial intelligence research fueling China’s technological rise, as well as the advanced military equipment underpinning Russia’s invasion of Ukraine.

The US government relies on private-sector firms to implement those export controls. It’s not working. US-manufactured semiconductors have been found in Russian weapons. And China is skirting American export controls to accelerate AI research and development, with the explicit goal of enhancing its military capabilities.

American semiconductor firms are unwilling or unable to restrict the flow of semiconductors. Instead of investing in effective compliance mechanisms, these firms have consistently prioritized their bottom lines—a rational decision, given the fundamentally risky nature of the semiconductor industry.

We can’t afford to wait for semiconductor firms to catch up gradually. To create a robust regulatory environment in the semiconductor industry, both the US government and chip companies must take clear and decisive actions today and consistently over time.

Consider the financial services industry. Those companies are also heavily regulated, implementing US government regulations ranging from international sanctions to anti-money laundering. For decades, these companies have invested heavily in compliance technology. Large banks maintain teams of compliance employees, often numbering in the thousands.

The companies understand that by entering the financial services industry, they assume the responsibility to verify their customers’ identities and activities, refuse services to those engaged in criminal activity, and report certain activities to the authorities. They take these obligations seriously because they know they will face massive fines when they fail. Across the financial sector, the Securities and Exchange Commission imposed a whopping $6.4 billion in penalties in 2022. For example, TD Bank recently paid almost $2 billion in penalties because of its ineffective anti-money laundering efforts

An executive order issued earlier this year applied a similar regulatory model to potential “know your customer” obligations for certain cloud service providers.

If Trump’s new license-focused export controls are to be effective, the administration must increase the penalties for noncompliance. The Commerce Department’s Bureau of Industry and Security (BIS) needs to more aggressively enforce its regulations by sharply increasing penalties for export control violations.

BIS has been working to improve enforcement, as evidenced by this week’s news of a $95 million penalty against Cadence Design Systems for violating export controls on its chip design technology. Unfortunately, BIS lacks the people, technology, and funding to enforce these controls across the board.

The Trump administration should also use its bully pulpit, publicly naming companies that break the rules and encouraging American firms and consumers to do business elsewhere. Regulatory threats and bad publicity are the only ways to force the semiconductor industry to take export control regulations seriously and invest in compliance.

With those threats in place, American semiconductor firms must accept their obligation to comply with regulations and cooperate. They need to invest in strengthening their compliance teams and conduct proactive audits of their subsidiaries, their customers, and their customers’ customers.

Firms should elevate risk and compliance voices onto their executive leadership teams, similar to the chief risk officer role found in banks. Senior leaders need to devote their time to regular progress reviews focused on meaningful, proactive compliance with export controls and other critical regulations, thereby leading their organizations to make compliance a priority.

As the world becomes increasingly dangerous and America’s adversaries become more emboldened, we need to maintain stronger control over our supply of critical semiconductors. If Russia and China are allowed unfettered access to advanced American chips for their AI efforts and military equipment, we risk losing the military advantage and our ability to deter conflicts worldwide. The geopolitical importance of semiconductors will only increase as the world becomes more dangerous and more reliant on advanced technologies—American security depends on limiting their flow.

This essay was written with Andrew Kidd and Celine Lee, and originally appeared in The National Interest.

“Encryption Backdoors and the Fourth Amendment”

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2025/07/encryption-backdoors-and-the-fourth-amendment.html

Law journal article that looks at the Dual_EC_PRNG backdoor from a US constitutional perspective:

Abstract: The National Security Agency (NSA) reportedly paid and pressured technology companies to trick their customers into using vulnerable encryption products. This Article examines whether any of three theories removed the Fourth Amendment’s requirement that this be reasonable. The first is that a challenge to the encryption backdoor might fail for want of a search or seizure. The Article rejects this both because the Amendment reaches some vulnerabilities apart from the searches and seizures they enable and because the creation of this vulnerability was itself a search or seizure. The second is that the role of the technology companies might have brought this backdoor within the private-search doctrine. The Article criticizes the doctrine­ particularly its origins in Burdeau v. McDowell­and argues that if it ever should apply, it should not here. The last is that the customers might have waived their Fourth Amendment rights under the third-party doctrine. The Article rejects this both because the customers were not on notice of the backdoor and because historical understandings of the Amendment would not have tolerated it. The Article concludes that none of these theories removed the Amendment’s reasonableness requirement.

AI-Generated Law

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2025/05/ai-generated-law.html

On April 14, Dubai’s ruler, Sheikh Mohammed bin Rashid Al Maktoum, announced that the United Arab Emirates would begin using artificial intelligence to help write its laws. A new Regulatory Intelligence Office would use the technology to “regularly suggest updates” to the law and “accelerate the issuance of legislation by up to 70%.” AI would create a “comprehensive legislative plan” spanning local and federal law and would be connected to public administration, the courts, and global policy trends.

The plan was widely greeted with astonishment. This sort of AI legislating would be a global “first,” with the potential to go “horribly wrong.” Skeptics fear that the AI model will make up facts or fundamentally fail to understand societal tenets such as fair treatment and justice when influencing law.

The truth is, the UAE’s idea of AI-generated law is not really a first and not necessarily terrible.

The first instance of enacted law known to have been written by AI was passed in Porto Alegre, Brazil, in 2023. It was a local ordinance about water meter replacement. Council member Ramiro Rosário was simply looking for help in generating and articulating ideas for solving a policy problem, and ChatGPT did well enough that the bill passed unanimously. We approve of AI assisting humans in this manner, although Rosário should have disclosed that the bill was written by AI before it was voted on.

Brazil was a harbinger but hardly unique. In recent years, there has been a steady stream of attention-seeking politicians at the local and national level introducing bills that they promote as being drafted by AI or letting AI write their speeches for them or even vocalize them in the chamber.

The Emirati proposal is different from those examples in important ways. It promises to be more systemic and less of a one-off stunt. The UAE has promised to spend more than $3 billion to transform into an “AI-native” government by 2027. Time will tell if it is also different in being more hype than reality.

Rather than being a true first, the UAE’s announcement is emblematic of a much wider global trend of legislative bodies integrating AI assistive tools for legislative research, drafting, translation, data processing, and much more. Individual lawmakers have begun turning to AI drafting tools as they traditionally have relied on staffers, interns, or lobbyists. The French government has gone so far as to train its own AI model to assist with legislative tasks.

Even asking AI to comprehensively review and update legislation would not be a first. In 2020, the U.S. state of Ohio began using AI to do wholesale revision of its administrative law. AI’s speed is potentially a good match to this kind of large-scale editorial project; the state’s then-lieutenant governor, Jon Husted, claims it was successful in eliminating 2.2 million words’ worth of unnecessary regulation from Ohio’s code. Now a U.S. senator, Husted has recently proposed to take the same approach to U.S. federal law, with an ideological bent promoting AI as a tool for systematic deregulation.

The dangers of confabulation and inhumanity—while legitimate—aren’t really what makes the potential of AI-generated law novel. Humans make mistakes when writing law, too. Recall that a single typo in a 900-page law nearly brought down the massive U.S. health care reforms of the Affordable Care Act in 2015, before the Supreme Court excused the error. And, distressingly, the citizens and residents of nondemocratic states are already subject to arbitrary and often inhumane laws. (The UAE is a federation of monarchies without direct elections of legislators and with a poor record on political rights and civil liberties, as evaluated by Freedom House.)

The primary concern with using AI in lawmaking is that it will be wielded as a tool by the powerful to advance their own interests. AI may not fundamentally change lawmaking, but its superhuman capabilities have the potential to exacerbate the risks of power concentration.

AI, and technology generally, is often invoked by politicians to give their project a patina of objectivity and rationality, but it doesn’t really do any such thing. As proposed, AI would simply give the UAE’s hereditary rulers new tools to express, enact, and enforce their preferred policies.

Mohammed’s emphasis that a primary benefit of AI will be to make law faster is also misguided. The machine may write the text, but humans will still propose, debate, and vote on the legislation. Drafting is rarely the bottleneck in passing new law. What takes much longer is for humans to amend, horse-trade, and ultimately come to agreement on the content of that legislation—even when that politicking is happening among a small group of monarchic elites.

Rather than expeditiousness, the more important capability offered by AI is sophistication. AI has the potential to make law more complex, tailoring it to a multitude of different scenarios. The combination of AI’s research and drafting speed makes it possible for it to outline legislation governing dozens, even thousands, of special cases for each proposed rule.

But here again, this capability of AI opens the door for the powerful to have their way. AI’s capacity to write complex law would allow the humans directing it to dictate their exacting policy preference for every special case. It could even embed those preferences surreptitiously.

Since time immemorial, legislators have carved out legal loopholes to narrowly cater to special interests. AI will be a powerful tool for authoritarians, lobbyists, and other empowered interests to do this at a greater scale. AI can help automatically produce what political scientist Amy McKay has termed “microlegislation“: loopholes that may be imperceptible to human readers on the page—until their impact is realized in the real world.

But AI can be constrained and directed to distribute power rather than concentrate it. For Emirati residents, the most intriguing possibility of the AI plan is the promise to introduce AI “interactive platforms” where the public can provide input to legislation. In experiments across locales as diverse as Kentucky, Massachusetts, France, Scotland, Taiwan, and many others, civil society within democracies are innovating and experimenting with ways to leverage AI to help listen to constituents and construct public policy in a way that best serves diverse stakeholders.

If the UAE is going to build an AI-native government, it should do so for the purpose of empowering people and not machines. AI has real potential to improve deliberation and pluralism in policymaking, and Emirati residents should hold their government accountable to delivering on this promise.

AI Will Write Complex Laws

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2025/01/ai-will-write-complex-laws.html

Artificial intelligence (AI) is writing law today. This has required no changes in legislative procedure or the rules of legislative bodies—all it takes is one legislator, or legislative assistant, to use generative AI in the process of drafting a bill.

In fact, the use of AI by legislators is only likely to become more prevalent. There are currently projects in the US House, US Senate, and legislatures around the world to trial the use of AI in various ways: searching databases, drafting text, summarizing meetings, performing policy research and analysis, and more. A Brazilian municipality passed the first known AI-written law in 2023.

That’s not surprising; AI is being used more everywhere. What is coming into focus is how policymakers will use AI and, critically, how this use will change the balance of power between the legislative and executive branches of government. Soon, US legislators may turn to AI to help them keep pace with the increasing complexity of their lawmaking—and this will suppress the power and discretion of the executive branch to make policy.

Demand for Increasingly Complex Legislation

Legislators are writing increasingly long, intricate, and complicated laws that human legislative drafters have trouble producing. Already in the US, the multibillion-dollar lobbying industry is subsidizing lawmakers in writing baroque laws: suggesting paragraphs to add to bills, specifying benefits for some, carving out exceptions for others. Indeed, the lobbying industry is growing in complexity and influence worldwide.

Several years ago, researchers studied bills introduced into state legislatures throughout the US, looking at which bills were wholly original texts and which borrowed text from other states or from lobbyist-written model legislation. Their conclusion was not very surprising. Those who borrowed the most text were in legislatures that were less resourced. This makes sense: If you’re a part-time legislator, perhaps unpaid and without a lot of staff, you need to rely on more external support to draft legislation. When the scope of policymaking outstrips the resources of legislators, they look for help. Today, that often means lobbyists, who provide expertise, research services, and drafting labor to legislators at the local, state, and federal levels at no charge. Of course, they are not unbiased: They seek to exert influence on behalf of their clients.

Another study, at the US federal level, measured the complexity of policies proposed in legislation and tried to determine the factors that led to such growing complexity. While there are numerous ways to measure legal complexity, these authors focused on the specificity of institutional design: How exacting is Congress in laying out the relational network of branches, agencies, and officials that will share power to implement the policy?

In looking at bills enacted between 1993 and 2014, the researchers found two things. First, they concluded that ideological polarization drives complexity. The suggestion is that if a legislator is on the extreme end of the ideological spectrum, they’re more likely to introduce a complex law that constrains the discretion of, as the authors put it, “entrenched bureaucratic interests.” And second, they found that divided government drives complexity to a large degree: Significant legislation passed under divided government was found to be 65 percent more complex than similar legislation passed under unified government. Their conclusion is that, if a legislator’s party controls Congress, and the opposing party controls the White House, the legislator will want to give the executive as little wiggle room as possible. When legislators’ preferences disagree with the executive’s, the legislature is incentivized to write laws that specify all the details. This gives the agency designated to implement the law as little discretion as possible.

Because polarization and divided government are increasingly entrenched in the US, the demand for complex legislation at the federal level is likely to grow. Today, we have both the greatest ideological polarization in Congress in living memory and an increasingly divided government at the federal level. Between 1900 and 1970 (57th through 90th Congresses), we had 27 instances of unified government and only seven divided; nearly a four-to-one ratio. Since then, the trend is roughly the opposite. As of the start of the next Congress, we will have had 20 divided governments and only eight unified (nearly a three-to-one ratio). And while the incoming Trump administration will see a unified government, the extremely closely divided House may often make this Congress look and feel like a divided one (see the recent government shutdown crisis as an exemplar) and makes truly divided government a strong possibility in 2027.

Another related factor driving the complexity of legislation is the need to do it all at once. The lobbyist feeding frenzy—spurring major bills like the Affordable Care Act to be thousands of pages in length—is driven in part by gridlock in Congress. Congressional productivity has dropped so low that bills on any given policy issue seem like a once-in-a-generation opportunity for legislators—and lobbyists—to set policy.

These dynamics also impact the states. States often have divided governments, albeit less often than they used to, and their demand for drafting assistance is arguably higher due to their significantly smaller staffs. And since the productivity of Congress has cratered in recent years, significantly more policymaking is happening at the state level.

But there’s another reason, particular to the US federal government, that will likely force congressional legislation to be more complex even during unified government. In June 2024, the US Supreme Court overturned the Chevron doctrine, which gave executive agencies broad power to specify and implement legislation. Suddenly, there is a mandate from the Supreme Court for more specific legislation. Issues that have historically been left implicitly to the executive branch are now required to be either explicitly delegated to agencies or specified directly in statute. Either way, the Court’s ruling implied that law should become more complex and that Congress should increase its policymaking capacity.

This affects the balance of power between the executive and legislative branches of government. When the legislature delegates less to the executive branch, it increases its own power. Every decision made explicitly in statute is a decision the executive makes not on its own but, rather, according to the directive of the legislature. In the US system of separation of powers, administrative law is a tool for balancing power among the legislative, executive, and judicial branches. The legislature gets to decide when to delegate and when not to, and it can respond to judicial review to adjust its delegation of control as needed. The elimination of Chevron will induce the legislature to exert its control over delegation more robustly.

At the same time, there are powerful political incentives for Congress to be vague and to rely on someone else, like agency bureaucrats, to make hard decisions. That empowers third parties—the corporations, or lobbyists—that have been gifted by the overturning of Chevron a new tool in arguing against administrative regulations not specifically backed up by law. A continuing stream of Supreme Court decisions handing victories to unpopular industries could be another driver of complex law, adding political pressure to pass legislative fixes.

AI Can Supply Complex Legislation

Congress may or may not be up to the challenge of putting more policy details into law, but the external forces outlined above—lobbyists, the judiciary, and an increasingly divided and polarized government—are pushing them to do so. When Congress does take on the task of writing complex legislation, it’s quite likely it will turn to AI for help.

Two particular AI capabilities enable Congress to write laws different from laws humans tend to write. One, AI models have an enormous scope of expertise, whereas people have only a handful of specializations. Large language models (LLMs) like the one powering ChatGPT can generate legislative text on funding specialty crop harvesting mechanization equally as well as material on energy efficiency standards for street lighting. This enables a legislator to address more topics simultaneously. Two, AI models have the sophistication to work with a higher degree of complexity than people can. Modern LLM systems can instantaneously perform several simultaneous multistep reasoning tasks using information from thousands of pages of documents. This enables a legislator to fill in more baroque detail on any given topic.

That’s not to say that handing over legislative drafting to machines is easily done. Modernizing any institutional process is extremely hard, even when the technology is readily available and performant. And modern AI still has a ways to go to achieve mastery of complex legal and policy issues. But the basic tools are there.

AI can be used in each step of lawmaking, and this will bring various benefits to policymakers. It could let them work on more policies—more bills—at the same time, add more detail and specificity to each bill, or interpret and incorporate more feedback from constituents and outside groups. The addition of a single AI tool to a legislative office may have an impact similar to adding several people to their staff, but with far lower cost.

Speed sometimes matters when writing law. When there is a change of governing party, there is often a rush to change as much policy as possible to match the platform of the new regime. AI could help legislators do that kind of wholesale revision. The result could be policy that is more responsive to voters—or more political instability. Already in 2024, the US House’s Office of the Clerk has begun using AI to speed up the process of producing cost estimates for bills and understanding how new legislation relates to existing code. Ohio has used an AI tool to do wholesale revision of state administrative law since 2020.

AI can also make laws clearer and more consistent. With their superhuman attention spans, AI tools are good at enforcing syntactic and grammatical rules. They will be effective at drafting text in precise and proper legislative language, or offering detailed feedback to human drafters. Borrowing ideas from software development, where coders use tools to identify common instances of bad programming practices, an AI reviewer can highlight bad law-writing practices. For example, it can detect when significant phrasing is inconsistent across a long bill. If a bill about insurance repeatedly lists a variety of disaster categories, but leaves one out one time, AI can catch that.

Perhaps this seems like minutiae, but a small ambiguity or mistake in law can have massive consequences. In 2015, the Affordable Care Act came close to being struck down because of a typo in four words, imperiling health care services extended to more than 7 million Americans.

There’s more that AI can do in the legislative process. AI can summarize bills and answer questions about their provisions. It can highlight aspects of a bill that align with, or are contrary to, different political points of view. We can even imagine a future in which AI can be used to simulate a new law and determine whether or not it would be effective, or what the side effects would be. This means that beyond writing them, AI could help lawmakers understand laws. Congress is notorious for producing bills hundreds of pages long, and many other countries sometimes have similarly massive omnibus bills that address many issues at once. It’s impossible for any one person to understand how each of these bills’ provisions would work. Many legislatures employ human analysis in budget or fiscal offices that analyze these bills and offer reports. AI could do this kind of work at greater speed and scale, so legislators could easily query an AI tool about how a particular bill would affect their district or areas of concern.

This is a use case that the House subcommittee on modernization has urged the Library of Congress to take action on. Numerous software vendors are already marketing AI legislative analysis tools. These tools can potentially find loopholes or, like the human lobbyists of today, craft them to benefit particular private interests.

These capabilities will be attractive to legislators who are looking to expand their power and capabilities but don’t necessarily have more funding to hire human staff. We should understand the idea of AI-augmented lawmaking contextualized within the longer history of legislative technologies. To serve society at modern scales, we’ve had to come a long way from the Athenian ideals of direct democracy and sortition. Democracy no longer involves just one person and one vote to decide a policy. It involves hundreds of thousands of constituents electing one representative, who is augmented by a staff as well as subsidized by lobbyists, and who implements policy through a vast administrative state coordinated by digital technologies. Using AI to help those representatives specify and refine their policy ideas is part of a long history of transformation.

Whether all this AI augmentation is good for all of us subject to the laws they make is less clear. There are real risks to AI-written law, but those risks are not dramatically different from what we endure today. AI-written law trying to optimize for certain policy outcomes may get it wrong (just as many human-written laws are misguided). AI-written law may be manipulated to benefit one constituency over others, by the tech companies that develop the AI, or by the legislators who apply it, just as human lobbyists steer policy to benefit their clients.

Regardless of what anyone thinks of any of this, regardless of whether it will be a net positive or a net negative, AI-made legislation is coming—the growing complexity of policy demands it. It doesn’t require any changes in legislative procedures or agreement from any rules committee. All it takes is for one legislative assistant, or lobbyist, to fire up a chatbot and ask it to create a draft. When legislators voted on that Brazilian bill in 2023, they didn’t know it was AI-written; the use of ChatGPT was undisclosed. And even if they had known, it’s not clear it would have made a difference. In the future, as in the past, we won’t always know which laws will have good impacts and which will have bad effects, regardless of the words on the page, or who (or what) wrote them.

This essay was written with Nathan E. Sanders, and originally appeared in Lawfare.

California AI Safety Bill Vetoed

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2024/10/california-ai-safety-bill-vetoed.html

Governor Newsom has vetoed the state’s AI safety bill.

I have mixed feelings about the bill. There’s a lot to like about it, and I want governments to regulate in this space. But, for now, it’s all EU.

(Related, the Council of Europe treaty on AI is ready for signature. It’ll be legally binding when signed, and it’s a big deal.)

AI and the 2024 US Elections

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2024/09/ai-and-the-2024-us-elections.html

For years now, AI has undermined the public’s ability to trust what it sees, hears, and reads. The Republican National Committee released a provocative ad offering an “AI-generated look into the country’s possible future if Joe Biden is re-elected,” showing apocalyptic, machine-made images of ruined cityscapes and chaos at the border. Fake robocalls purporting to be from Biden urged New Hampshire residents not to vote in the 2024 primary election. This summer, the Department of Justice cracked down on a Russian bot farm that was using AI to impersonate Americans on social media, and OpenAI disrupted an Iranian group using ChatGPT to generate fake social-media comments.

It’s not altogether clear what damage AI itself may cause, though the reasons for concern are obvious—the technology makes it easier for bad actors to construct highly persuasive and misleading content. With that risk in mind, there has been some movement toward constraining the use of AI, yet progress has been painstakingly slow in the area where it may count most: the 2024 election.

Two years ago, the Biden administration issued a blueprint for an AI Bill of Rights aiming to address “unsafe or ineffective systems,” “algorithmic discrimination,” and “abusive data practices,” among other things. Then, last year, Biden built on that document when he issued his executive order on AI. Also in 2023, Senate Majority Leader Chuck Schumer held an AI summit in Washington that included the centibillionaires Bill Gates, Mark Zuckerberg, and Elon Musk. Several weeks later, the United Kingdom hosted an international AI Safety Summit that led to the serious-sounding “Bletchley Declaration,” which urged international cooperation on AI regulation. The risks of AI fakery in elections have not sneaked up on anybody.

Yet none of this has resulted in changes that would resolve the use of AI in U.S. political campaigns. Even worse, the two federal agencies with a chance to do something about it have punted the ball, very likely until after the election.

On July 25, the Federal Communications Commission issued a proposal that would require political advertisements on TV and radio to disclose if they used AI. (The FCC has no jurisdiction over streaming, social media, or web ads.) That seems like a step forward, but there are two big problems. First, the proposed rules, even if enacted, are unlikely to take effect before early voting starts in this year’s election. Second, the proposal immediately devolved into a partisan slugfest. A Republican FCC commissioner alleged that the Democratic National Committee was orchestrating the rule change because Democrats are falling behind the GOP in using AI in elections. Plus, he argued, this was the Federal Election Commission’s job to do.

Yet last month, the FEC announced that it won’t even try making new rules against using AI to impersonate candidates in campaign ads through deepfaked audio or video. The FEC also said that it lacks the statutory authority to make rules about misrepresentations using deepfaked audio or video. And it lamented that it lacks the technical expertise to do so, anyway. Then, last week, the FEC compromised, announcing that it intends to enforce its existing rules against fraudulent misrepresentation regardless of what technology it is conducted with. Advocates for stronger rules on AI in campaign ads, such as Public Citizen, did not find this nearly sufficient, characterizing it as a “wait-and-see approach” to handling “electoral chaos.”

Perhaps this is to be expected: The freedom of speech guaranteed by the First Amendment generally permits lying in political ads. But the American public has signaled that it would like some rules governing AI’s use in campaigns. In 2023, more than half of Americans polled responded that the federal government should outlaw all uses of AI-generated content in political ads. Going further, in 2024, about half of surveyed Americans said they thought that political candidates who intentionally manipulated audio, images, or video should be prevented from holding office or removed if they had won an election. Only 4 percent thought there should be no penalty at all.

The underlying problem is that Congress has not clearly given any agency the responsibility to keep political advertisements grounded in reality, whether in response to AI or old-fashioned forms of disinformation. The Federal Trade Commission has jurisdiction over truth in advertising, but political ads are largely exempt—again, part of our First Amendment tradition. The FEC’s remit is campaign finance, but the Supreme Court has progressively stripped its authorities. Even where it could act, the commission is often stymied by political deadlock. The FCC has more evident responsibility for regulating political advertising, but only in certain media: broadcast, robocalls, text messages. Worse yet, the FCC’s rules are not exactly robust. It has actually loosened rules on political spam over time, leading to the barrage of messages many receive today. (That said, in February, the FCC did unanimously rule that robocalls using AI voice-cloning technology, like the Biden ad in New Hampshire, are already illegal under a 30-year-old law.)

It’s a fragmented system, with many important activities falling victim to gaps in statutory authority and a turf war between federal agencies. And as political campaigning has gone digital, it has entered an online space with even fewer disclosure requirements or other regulations. No one seems to agree where, or whether, AI is under any of these agencies’ jurisdictions. In the absence of broad regulation, some states have made their own decisions. In 2019, California was the first state in the nation to prohibit the use of deceptively manipulated media in elections, and has strengthened these protections with a raft of newly passed laws this fall. Nineteen states have now passed laws regulating the use of deepfakes in elections.

One problem that regulators have to contend with is the wide applicability of AI: The technology can simply be used for many different things, each one demanding its own intervention. People might accept a candidate digitally airbrushing their photo to look better, but not doing the same thing to make their opponent look worse. We’re used to getting personalized campaign messages and letters signed by the candidate; is it okay to get a robocall with a voice clone of the same politician speaking our name? And what should we make of the AI-generated campaign memes now shared by figures such as Musk and Donald Trump?

Despite the gridlock in Congress, these are issues with bipartisan interest. This makes it conceivable that something might be done, but probably not until after the 2024 election and only if legislators overcome major roadblocks. One bill under consideration, the AI Transparency in Elections Act, would instruct the FEC to require disclosure when political advertising uses media generated substantially by AI. Critics say, implausibly, that the disclosure is onerous and would increase the cost of political advertising. The Honest Ads Act would modernize campaign-finance law, extending FEC authority to definitively encompass digital advertising. However, it has languished for years because of reported opposition from the tech industry. The Protect Elections From Deceptive AI Act would ban materially deceptive AI-generated content from federal elections, as in California and other states. These are promising proposals, but libertarian and civil-liberties groups are already signaling challenges to all of these on First Amendment grounds. And, vexingly, at least one FEC commissioner has directly cited congressional consideration of some of these bills as a reason for his agency not to act on AI in the meantime.

One group that benefits from all this confusion: tech platforms. When few or no evident rules govern political expenditures online and uses of new technologies like AI, tech companies have maximum latitude to sell ads, services, and personal data to campaigns. This is reflected in their lobbying efforts, as well as the voluntary policy restraints they occasionally trumpet to convince the public they don’t need greater regulation.

Big Tech has demonstrated that it will uphold these voluntary pledges only if they benefit the industry. Facebook once, briefly, banned political advertising on its platform. No longer; now it even allows ads that baselessly deny the outcome of the 2020 presidential election. OpenAI’s policies have long prohibited political campaigns from using ChatGPT, but those restrictions are trivial to evade. Several companies have volunteered to add watermarks to AI-generated content, but they are easily circumvented. Watermarks might even make disinformation worse by giving the false impression that non-watermarked images are legitimate.

This important public policy should not be left to corporations, yet Congress seems resigned not to act before the election. Schumer hinted to NBC News in August that Congress may try to attach deepfake regulations to must-pass funding or defense bills this month to ensure that they become law before the election. More recently, he has pointed to the need for action “beyond the 2024 election.”

The three bills listed above are worthwhile, but they are just a start. The FEC and FCC should not be left to snipe with each other about what territory belongs to which agency. And the FEC needs more significant, structural reform to reduce partisan gridlock and enable it to get more done. We also need transparency into and governance of the algorithmic amplification of misinformation on social-media platforms. That requires that the pervasive influence of tech companies and their billionaire investors should be limited through stronger lobbying and campaign-finance protections.

Our regulation of electioneering never caught up to AOL, let alone social media and AI. And deceiving videos harm our democratic process, whether they are created by AI or actors on a soundstage. But the urgent concern over AI should be harnessed to advance legislative reform. Congress needs to do more than stick a few fingers in the dike to control the coming tide of election disinformation. It needs to act more boldly to reshape the landscape of regulation for political campaigning.

This essay was written with Nathan Sanders, and originally appeared in The Atlantic.

Australia Threatens to Force Companies to Break Encryption

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2024/09/australia-threatens-to-force-companies-to-break-encryption.html

In 2018, Australia passed the Assistance and Access Act, which—among other things—gave the government the power to force companies to break their own encryption.

The Assistance and Access Act includes key components that outline investigatory powers between government and industry. These components include:

  • Technical Assistance Requests (TARs): TARs are voluntary requests for assistance accessing encrypted data from law enforcement to teleco and technology companies. Companies are not legally obligated to comply with a TAR but law enforcement sends requests to solicit cooperation.
  • Technical Assistance Notices (TANs): TANS are compulsory notices (such as computer access warrants) that require companies to assist within their means with decrypting data or providing technical information that a law enforcement agency cannot access independently. Examples include certain source code, encryption, cryptography, and electronic hardware.
  • Technical Capability Notices (TCNs): TCNs are orders that require a company to build new capabilities that assist law enforcement agencies in accessing encrypted data. The Attorney-General must approve a TCN by confirming it is reasonable, proportionate, practical, and technically feasible.

It’s that final one that’s the real problem. The Australian government can force tech companies to build backdoors into their systems.

This is law, but near as anyone can tell the government has never used that third provision.

Now, the director of the Australian Security Intelligence Organisation (ASIO)—that’s basically their FBI or MI5—is threatening to do just that:

ASIO head, Mike Burgess, says he may soon use powers to compel tech companies to cooperate with warrants and unlock encrypted chats to aid in national security investigations.

[…]

But Mr Burgess says lawful access is all about targeted action against individuals under investigation.

“I understand there are people who really need it in some countries, but in this country, we’re subject to the rule of law, and if you’re doing nothing wrong, you’ve got privacy because no one’s looking at it,” Mr Burgess said.

“If there are suspicions, or we’ve got proof that we can justify you’re doing something wrong and you must be investigated, then actually we want lawful access to that data.”

Mr Burgess says tech companies could design apps in a way that allows law enforcement and security agencies access when they request it without comprising the integrity of encryption.

“I don’t accept that actually lawful access is a back door or systemic weakness, because that, in my mind, will be a bad design. I believe you can ­ these are clever people ­ design things that are secure, that give secure, lawful access,” he said.

We in the encryption space call that last one “nerd harder.” It, and the rest of his remarks, are the same tired talking points we’ve heard again and again.

It’s going to be an awfully big mess if Australia actually tries to make Apple, or Facebook’s WhatsApp, for that matter, break its own encryption for its “targeted actions” that put every other user at risk.

US Federal Court Rules Against Geofence Warrants

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2024/08/us-federal-court-rules-against-geofence-warrants.html

This is a big deal. A US Appeals Court ruled that geofence warrants—these are general warrants demanding information about all people within a geographical boundary—are unconstitutional.

The decision seems obvious to me, but you can’t take anything for granted.

How AI Will Change Democracy

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2024/05/how-ai-will-change-democracy.html

I don’t think it’s an exaggeration to predict that artificial intelligence will affect every aspect of our society. Not by doing new things. But mostly by doing things that are already being done by humans, perfectly competently.

Replacing humans with AIs isn’t necessarily interesting. But when an AI takes over a human task, the task changes.

In particular, there are potential changes over four dimensions: Speed, scale, scope and sophistication. The problem with AIs trading stocks isn’t that they’re better than humans—it’s that they’re faster. But computers are better at chess and Go because they use more sophisticated strategies than humans. We’re worried about AI-controlled social media accounts because they operate on a superhuman scale.

It gets interesting when changes in degree can become changes in kind. High-speed trading is fundamentally different than regular human trading. AIs have invented fundamentally new strategies in the game of Go. Millions of AI-controlled social media accounts could fundamentally change the nature of propaganda.

It’s these sorts of changes and how AI will affect democracy that I want to talk about.

To start, I want to list some of AI’s core competences. First, it is really good as a summarizer. Second, AI is good at explaining things, teaching with infinite patience. Third, and related, AI can persuade. Propaganda is an offshoot of this. Fourth, AI is fundamentally a prediction technology. Predictions about whether turning left or right will get you to your destination faster. Predictions about whether a tumor is cancerous might improve medical diagnoses. Predictions about which word is likely to come next can help compose an email. Fifth, AI can assess. Assessing requires outside context and criteria. AI is less good at assessing, but it’s getting better. Sixth, AI can decide. A decision is a prediction plus an assessment. We are already using AI to make all sorts of decisions.

How these competences translate to actual useful AI systems depends a lot on the details. We don’t know how far AI will go in replicating or replacing human cognitive functions. Or how soon that will happen. In constrained environments it can be easy. AIs already play chess and Go better than humans. Unconstrained environments are harder. There are still significant challenges to fully AI-piloted automobiles. The technologist Jaron Lanier has a nice quote, that AI does best when “human activities have been done many times before, but not in exactly the same way.”

In this talk, I am going to be largely optimistic about the technology. I’m not going to dwell on the details of how the AI systems might work. Much of what I am talking about is still in the future. Science fiction, but not unrealistic science fiction.

Where I am going to be less optimistic—and more realistic—is about the social implications of the technology. Again, I am less interested in how AI will substitute for humans. I’m looking more at the second-order effects of those substitutions: How the underlying systems will change because of changes in speed, scale, scope and sophistication. My goal is to imagine the possibilities. So that we might be prepared for their eventuality.

And as I go through the possibilities, keep in mind a few questions: Will the change distribute or consolidate power? Will it make people more or less personally involved in democracy? What needs to happen before people will trust AI in this context? What could go wrong if a bad actor subverted the AI in this context? And what can we do, as security technologists, to help?

I am thinking about democracy very broadly. Not just representations, or elections. Democracy as a system for distributing decisions evenly across a population. It’s a way of converting individual preferences into group decisions. And that includes bureaucratic decisions.

To that end, I want to discuss five different areas where AI will affect democracy: Politics, lawmaking, administration, the legal system and, finally, citizens themselves.

I: AI-assisted politicians

I’ve already said that AIs are good at persuasion. Politicians will make use of that. Pretty much everyone talks about AI propaganda. Politicians will make use of that, too. But let’s talk about how this might go well.

In the past, candidates would write books and give speeches to connect with voters. In the future, candidates will also use personalized chatbots to directly engage with voters on a variety of issues. AI can also help fundraise. I don’t have to explain the persuasive power of individually crafted appeals. AI can conduct polls. There’s some really interesting work into having large language models assume different personas and answer questions from their points of view. Unlike people, AIs are always available, will answer thousands of questions without getting tired or bored and are more reliable. This won’t replace polls, but it can augment them. AI can assist human campaign managers by coordinating campaign workers, creating talking points, doing media outreach and assisting get-out-the-vote efforts. These are all things that humans already do. So there’s no real news there.

The changes are largely in scale. AIs can engage with voters, conduct polls and fundraise at a scale that humans cannot—for all sizes of elections. They can also assist in lobbying strategies. AIs could also potentially develop more sophisticated campaign and political strategies than humans can. I expect an arms race as politicians start using these sorts of tools. And we don’t know if the tools will favor one political ideology over another.

More interestingly, future politicians will largely be AI-driven. I don’t mean that AI will replace humans as politicians. Absent a major cultural shift—and some serious changes in the law—that won’t happen. But as AI starts to look and feel more human, our human politicians will start to look and feel more like AI. I think we will be OK with it, because it’s a path we’ve been walking down for a long time. Any major politician today is just the public face of a complex socio-technical system. When the president makes a speech, we all know that they didn’t write it. When a legislator sends out a campaign email, we know that they didn’t write that either—even if they signed it. And when we get a holiday card from any of these people, we know that it was signed by an autopen. Those things are so much a part of politics today that we don’t even think about it. In the future, we’ll accept that almost all communications from our leaders will be written by AI. We’ll accept that they use AI tools for making political and policy decisions. And for planning their campaigns. And for everything else they do. None of this is necessarily bad. But it does change the nature of politics and politicians—just like television and the internet did.

II: AI-assisted legislators

AIs are already good at summarization. This can be applied to listening to constituents:  summarizing letters, comments and making sense of constituent inputs. Public meetings might be summarized. Here the scale of the problem is already overwhelming, and AI can make a big difference. Beyond summarizing, AI can highlight interesting arguments or detect bulk letter-writing campaigns. They can aid in political negotiating.

AIs can also write laws. In November 2023, Porto Alegre, Brazil became the first city to enact a law that was entirely written by AI. It had to do with water meters. One of the councilmen prompted ChatGPT, and it produced a complete bill. He submitted it to the legislature without telling anyone who wrote it. And the humans passed it without any changes.

A law is just a piece of generated text that a government agrees to adopt. And as with every other profession, policymakers will turn to AI to help them draft and revise text. Also, AI can take human-written laws and figure out what they actually mean. Lots of laws are recursive, referencing paragraphs and words of other laws. AIs are already good at making sense of all that.

This means that AI will be good at finding legal loopholes—or at creating legal loopholes. I wrote about this in my latest book, A Hacker’s Mind. Finding loopholes is similar to finding vulnerabilities in software. There’s also a concept called “micro-legislation.” That’s the smallest unit of law that makes a difference to someone. It could be a word or a punctuation mark. AIs will be good at inserting micro-legislation into larger bills. More positively, AI can help figure out unintended consequences of a policy change—by simulating how the change interacts with all the other laws and with human behavior.

AI can also write more complex law than humans can. Right now, laws tend to be general. With details to be worked out by a government agency. AI can allow legislators to propose, and then vote on, all of those details. That will change the balance of power between the legislative and the executive branches of government. This is less of an issue when the same party controls the executive and the legislative branches. It is a big deal when those branches of government are in the hands of different parties. The worry is that AI will give the most powerful groups more tools for propagating their interests.

AI can write laws that are impossible for humans to understand. There are two kinds of laws: specific laws, like speed limits, and laws that require judgment, like those that address reckless driving. Imagine that we train an AI on lots of street camera footage to recognize reckless driving and that it gets better than humans at identifying the sort of behavior that tends to result in accidents. And because it has real-time access to cameras everywhere, it can spot it … everywhere. The AI won’t be able to explain its criteria: It would be a black-box neural net. But we could pass a law defining reckless driving by what that AI says. It would be a law that no human could ever understand. This could happen in all sorts of areas where judgment is part of defining what is illegal. We could delegate many things to the AI because of speed and scale. Market manipulation. Medical malpractice. False advertising. I don’t know if humans will accept this.

III: AI-assisted bureaucracy

Generative AI is already good at a whole lot of administrative paperwork tasks. It will only get better. I want to focus on a few places where it will make a big difference. It could aid in benefits administration—figuring out who is eligible for what. Humans do this today, but there is often a backlog because there aren’t enough humans. It could audit contracts. It could operate at scale, auditing all human-negotiated government contracts. It could aid in contracts negotiation. The government buys a lot of things and has all sorts of complicated rules. AI could help government contractors navigate those rules.

More generally, it could aid in negotiations of all kinds. Think of it as a strategic adviser. This is no different than a human but could result in more complex negotiations. Human negotiations generally center around only a few issues. Mostly because that’s what humans can keep in mind. AI versus AI negotiations could potentially involve thousands of variables simultaneously. Imagine we are using an AI to aid in some international trade negotiation and it suggests a complex strategy that is beyond human understanding. Will we blindly follow the AI? Will we be more willing to do so once we have some history with its accuracy?

And one last bureaucratic possibility: Could AI come up with better institutional designs than we have today? And would we implement them?

IV: AI-assisted legal system

When referring to an AI-assisted legal system, I mean this very broadly—both lawyering and judging and all the things surrounding those activities.

AIs can be lawyers. Early attempts at having AIs write legal briefs didn’t go well. But this is already changing as the systems get more accurate. Chatbots are now able to properly cite their sources and minimize errors. Future AIs will be much better at writing legalese, drastically reducing the cost of legal counsel. And there’s every indication that it will be able to do much of the routine work that lawyers do. So let’s talk about what this means.

Most obviously, it reduces the cost of legal advice and representation, giving it to people who currently can’t afford it. An AI public defender is going to be a lot better than an overworked not very good human public defender. But if we assume that human-plus-AI beats AI-only, then the rich get the combination, and the poor are stuck with just the AI.

It also will result in more sophisticated legal arguments. AI’s ability to search all of the law for precedents to bolster a case will be transformative.

AI will also change the meaning of a lawsuit. Right now, suing someone acts as a strong social signal because of the cost. If the cost drops to free, that signal will be lost. And orders of magnitude more lawsuits will be filed, which will overwhelm the court system.

Another effect could be gutting the profession. Lawyering is based on apprenticeship. But if most of the apprentice slots are filled by AIs, where do newly minted attorneys go to get training? And then where do the top human lawyers come from? This might not happen. AI-assisted lawyers might result in more human lawyering. We don’t know yet.

AI can help enforce the law. In a sense, this is nothing new. Automated systems already act as law enforcement—think speed trap cameras and Breathalyzers. But AI can take this kind of thing much further, like automatically identifying people who cheat on tax returns, identifying fraud on government service applications and watching all of the traffic cameras and issuing citations.

Again, the AI is performing a task for which we don’t have enough humans. And doing it faster, and at scale. This has the obvious problem of false positives. Which could be hard to contest if the courts believe that the computer is always right. This is a thing today: If a Breathalyzer says you’re drunk, it can be hard to contest the software in court. And also the problem of bias, of course: AI law enforcers may be more and less equitable than their human predecessors.

But most importantly, AI changes our relationship with the law. Everyone commits driving violations all the time. If we had a system of automatic enforcement, the way we all drive would change—significantly. Not everyone wants this future. Lots of people don’t want to fund the IRS, even though catching tax cheats is incredibly profitable for the government. And there are legitimate concerns as to whether this would be applied equitably.

AI can help enforce regulations. We have no shortage of rules and regulations. What we have is a shortage of time, resources and willpower to enforce them, which means that lots of companies know that they can ignore regulations with impunity. AI can change this by decoupling the ability to enforce rules from the resources necessary to do it. This makes enforcement more scalable and efficient. Imagine putting cameras in every slaughterhouse in the country looking for animal welfare violations or fielding an AI in every warehouse camera looking for labor violations. That could create an enormous shift in the balance of power between government and corporations—which means that it will be strongly resisted by corporate power.

AIs can provide expert opinions in court. Imagine an AI trained on millions of traffic accidents, including video footage, telemetry from cars and previous court cases. The AI could provide the court with a reconstruction of the accident along with an assignment of fault. AI could do this in a lot of cases where there aren’t enough human experts to analyze the data—and would do it better, because it would have more experience.

AIs can also perform judging tasks, weighing evidence and making decisions, probably not in actual courtrooms, at least not anytime soon, but in other contexts. There are many areas of government where we don’t have enough adjudicators. Automated adjudication has the potential to offer everyone immediate justice. Maybe the AI does the first level of adjudication and humans handle appeals. Probably the first place we’ll see this is in contracts. Instead of the parties agreeing to binding arbitration to resolve disputes, they’ll agree to binding arbitration by AI. This would significantly decrease cost of arbitration. Which would probably significantly increase the number of disputes.

So, let’s imagine a world where dispute resolution is both cheap and fast. If you and I are business partners, and we have a disagreement, we can get a ruling in minutes. And we can do it as many times as we want—multiple times a day, even. Will we lose the ability to disagree and then resolve our disagreements on our own? Or will this make it easier for us to be in a partnership and trust each other?

V: AI-assisted citizens

AI can help people understand political issues by explaining them. We can imagine both partisan and nonpartisan chatbots. AI can also provide political analysis and commentary. And it can do this at every scale. Including for local elections that simply aren’t important enough to attract human journalists. There is a lot of research going on right now on AI as moderator, facilitator, and consensus builder. Human moderators are still better, but we don’t have enough human moderators. And AI will improve over time. AI can moderate at scale, giving the capability to every decision-making group—or chatroom—or local government meeting.

AI can act as a government watchdog. Right now, much local government effectively happens in secret because there are no local journalists covering public meetings. AI can change that, providing summaries and flagging changes in position.

AIs can help people navigate bureaucracies by filling out forms, applying for services and contesting bureaucratic actions. This would help people get the services they deserve, especially disadvantaged people who have difficulty navigating these systems. Again, this is a task that we don’t have enough qualified humans to perform. It sounds good, but not everyone wants this. Administrative burdens can be deliberate.

Finally, AI can eliminate the need for politicians. This one is further out there, but bear with me. Already there is research showing AI can extrapolate our political preferences. An AI personal assistant trained on and continuously attuned to your political preferences could advise you, including what to support and who to vote for. It could possibly even vote on your behalf or, more interestingly, act as your personal representative.

This is where it gets interesting. Our system of representative democracy empowers elected officials to stand in for our collective preferences. But that has obvious problems. Representatives are necessary because people don’t pay attention to politics. And even if they did, there isn’t enough room in the debate hall for everyone to fit. So we need to pick one of us to pass laws in our name. But that selection process is incredibly inefficient. We have complex policy wants and beliefs and can make complex trade-offs. The space of possible policy outcomes is equally complex. But we can’t directly debate the policies. We can only choose one of two—or maybe a few more—candidates to do that for us. This has been called democracy’s “lossy bottleneck.” AI can change this. We can imagine a personal AI directly participating in policy debates on our behalf along with millions of other personal AIs and coming to a consensus on policy.

More near term, AIs can result in more ballot initiatives. Instead of five or six, there might be five or six hundred, as long as the AI can reliably advise people on how to vote. It’s hard to know whether this is a good thing. I don’t think we want people to become politically passive because the AI is taking care of it. But it could result in more legislation that the majority actually wants.

Where will AI take us?

That’s my list. Again, watch where changes of degree result in changes in kind. The sophistication of AI lawmaking will mean more detailed laws, which will change the balance of power between the executive and the legislative branches. The scale of AI lawyering means that litigation becomes affordable to everyone, which will mean an explosion in the amount of litigation. The speed of AI adjudication means that contract disputes will get resolved much faster, which will change the nature of settlements. The scope of AI enforcement means that some laws will become impossible to evade, which will change how the rich and powerful think about them.

I think this is all coming. The time frame is hazy, but the technology is moving in these directions.

All of these applications need security of one form or another. Can we provide confidentiality, integrity and availability where it is needed? AIs are just computers. As such, they have all the security problems regular computers have—plus the new security risks stemming from AI and the way it is trained, deployed and used. Like everything else in security, it depends on the details.

First, the incentives matter. In some cases, the user of the AI wants it to be both secure and accurate. In some cases, the user of the AI wants to subvert the system. Think about prompt injection attacks. In most cases, the owners of the AIs aren’t the users of the AI. As happened with search engines and social media, surveillance and advertising are likely to become the AI’s business model. And in some cases, what the user of the AI wants is at odds with what society wants.

Second, the risks matter. The cost of getting things wrong depends a lot on the application. If a candidate’s chatbot suggests a ridiculous policy, that’s easily corrected. If an AI is helping someone fill out their immigration paperwork, a mistake can get them deported. We need to understand the rate of AI mistakes versus the rate of human mistakes—and also realize that AI mistakes are viewed differently than human mistakes. There are also different types of mistakes: false positives versus false negatives. But also, AI systems can make different kinds of mistakes than humans do—and that’s important. In every case, the systems need to be able to correct mistakes, especially in the context of democracy.

Many of the applications are in adversarial environments. If two countries are using AI to assist in trade negotiations, they are both going to try to hack each other’s AIs. This will include attacks against the AI models but also conventional attacks against the computers and networks that are running the AIs. They’re going to want to subvert, eavesdrop on or disrupt the other’s AI.

Some AI applications will need to run in secure environments. Large language models work best when they have access to everything, in order to train. That goes against traditional classification rules about compartmentalization.

Fourth, power matters. AI is a technology that fundamentally magnifies power of the humans who use it, but not equally across users or applications. Can we build systems that reduce power imbalances rather than increase them? Think of the privacy versus surveillance debate in the context of AI.

And similarly, equity matters. Human agency matters.

And finally, trust matters. Whether or not to trust an AI is less about the AI and more about the application. Some of these AI applications are individual. Some of these applications are societal. Whether something like “fairness” matters depends on this. And there are many competing definitions of fairness that depend on the details of the system and the application. It’s the same with transparency. The need for it depends on the application and the incentives. Democratic applications are likely to require more transparency than corporate ones and probably AI models that are not owned and run by global tech monopolies.

All of these security issues are bigger than AI or democracy. Like all of our security experience, applying it to these new systems will require some new thinking.

AI will be one of humanity’s most important inventions. That’s probably true. What we don’t know is if this is the moment we are inventing it. Or if today’s systems are yet more over-hyped technologies. But these are security conversations we are going to need to have eventually.

AI is fundamentally a power-enhancing technology. We need to ensure that it distributes power and doesn’t further concentrate it.

AI is coming for democracy. Whether the changes are a net positive or negative depends on us. Let’s help tilt things to the positive.

This essay is adapted from a keynote speech delivered at the RSA Conference in San Francisco on May 7, 2024. It originally appeared in Cyberscoop.

 

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.