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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.
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 could 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.
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.
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
the2023-24 school year and the 2024–25school 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.
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.
It’s been a big month for US data privacy. Indiana, Iowa, and Tennessee all passed state privacy laws, bringing the total number of states with a privacy law up to eight. No private right of action in any of those, which means it’s up to the states to enforce the laws.
In an open letter, seven secure messaging apps—including Signal and WhatsApp—point out that the UK’s Online Safety Bill could destroy end-to-end encryption:
As currently drafted, the Bill could break end-to-end encryption,opening the door to routine, general and indiscriminate surveillance of personal messages of friends, family members, employees, executives, journalists, human rights activists and even politicians themselves, which would fundamentally undermine everyone’s ability to communicate securely.
The Bill provides no explicit protection for encryption, and if implemented as written, could empower OFCOM to try to force the proactive scanning of private messages on end-to-end encrypted communication services—nullifying the purpose of end-to-end encryption as a result and compromising the privacy of all users.
In short, the Bill poses an unprecedented threat to the privacy, safety and security of every UK citizen and the people with whom they communicate around the world, while emboldening hostile governments who may seek to draft copy-cat laws.
Both Signal and WhatsApp have said that they will cease services in the UK rather than compromise the security of their users worldwide.
EFF has a good explainer on the problems with the new UN Cybercrime Treaty, currently being negotiated in Vienna.
The draft treaty has the potential to rewrite criminal laws around the world, possibly adding over 30 criminal offenses and new expansive police powers for both domestic and international criminal investigations.
[…]
While we don’t think the U.N. Cybercrime Treaty is necessary, we’ve been closely scrutinizing the process and providing constructive analysis. We’ve made clear that human rights must be baked into the proposed treaty so that it doesn’t become a tool to stifle freedom of expression, infringe on privacy and data protection, or endanger vulnerable people and communities.
Here’s a religious hack:
You want to commit suicide, but it’s a mortal sin: your soul goes straight to hell, forever. So what you do is murder someone. That will get you executed, but if you confess your sins to a priest beforehand you avoid hell. Problem solved.
This was actually a problem in the 17th and 18th centuries in Northern Europe, particularly Denmark. And it remained a problem until capital punishment was abolished for murder.
It’s a clever hack. I didn’t learn about it in time to put it in my book, A Hacker’s Mind, but I have several other good hacks of religious rules.
Nearly 90% of the multibillion-dollar federal lobbying apparatus in the United States serves corporate interests. In some cases, the objective of that money is obvious. Google pours millions into lobbying on bills related to antitrust regulation. Big energy companies expect action whenever there is a move to end drilling leases for federal lands, in exchange for the tens of millions they contribute to congressional reelection campaigns.
But lobbying strategies are not always so blunt, and the interests involved are not always so obvious. Consider, for example, a 2013 Massachusetts bill that tried to restrict the commercial use of data collected from K-12 students using services accessed via the internet. The bill appealed to many privacy-conscious education advocates, and appropriately so. But behind the justification of protecting students lay a market-altering policy: the bill was introduced at the behest of Microsoft lobbyists, in an effort to exclude Google Docs from classrooms.
What would happen if such legal-but-sneaky strategies for tilting the rules in favor of one group over another become more widespread and effective? We can see hints of an answer in the remarkable pace at which artificial-intelligence tools for everything from writing to graphic design are being developed and improved. And the unavoidable conclusion is that AI will make lobbying more guileful, and perhaps more successful.
It turns out there is a natural opening for this technology: microlegislation.
“Microlegislation” is a term for small pieces of proposed law that cater—sometimes unexpectedly—to narrow interests. Political scientist Amy McKay coined the term. She studied the 564 amendments to the Affordable Care Act (“Obamacare”) considered by the Senate Finance Committee in 2009, as well as the positions of 866 lobbying groups and their campaign contributions. She documented instances where lobbyist comments—on health-care research, vaccine services, and other provisions—were translated directly into microlegislation in the form of amendments. And she found that those groups’ financial contributions to specific senators on the committee increased the amendments’ chances of passing.
Her finding that lobbying works was no surprise. More important, McKay’s work demonstrated that computer models can predict the likely fate of proposed legislative amendments, as well as the paths by which lobbyists can most effectively secure their desired outcomes. And that turns out to be a critical piece of creating an AI lobbyist.
Lobbying has long been part of the give-and-take among human policymakers and advocates working to balance their competing interests. The danger of microlegislation—a danger greatly exacerbated by AI—is that it can be used in a way that makes it difficult to figure out who the legislation truly benefits.
Another word for a strategy like this is a “hack.” Hacks follow the rules of a system but subvert their intent. Hacking is often associated with computer systems, but the concept is also applicable to social systems like financial markets, tax codes, and legislative processes.
While the idea of monied interests incorporating AI assistive technologies into their lobbying remains hypothetical, specific machine-learning technologies exist today that would enable them to do so. We should expect these techniques to get better and their utilization to grow, just as we’ve seen in so many other domains.
Here’s how it might work.
To make microlegislation, machine-learning systems must be able to uncover the smallest modification that could be made to a bill or existing law that would make the biggest impact on a narrow interest.
There are three basic challenges involved. First, you must create a policy proposal—small suggested changes to legal text—and anticipate whether or not a human reader would recognize the alteration as substantive. This is important; a change that isn’t detectable is more likely to pass without controversy. Second, you need to do an impact assessment to project the implications of that change for the short- or long-range financial interests of companies. Third, you need a lobbying strategizer to identify what levers of power to pull to get the best proposal into law.
Existing AI tools can tackle all three of these.
The first step, the policy proposal, leverages the core function of generative AI. Large language models, the sort that have been used for general-purpose chatbots such as ChatGPT, can easily be adapted to write like a native in different specialized domains after seeing a relatively small number of examples. This process is called fine-tuning. For example, a model “pre-trained” on a large library of generic text samples from books and the internet can be “fine-tuned” to work effectively on medical literature, computer science papers, and product reviews.
Given this flexibility and capacity for adaptation, a large language model could be fine-tuned to produce draft legislative texts, given a data set of previously offered amendments and the bills they were associated with. Training data is available. At the federal level, it’s provided by the US Government Publishing Office, and there are already tools for downloading and interacting with it. Most other jurisdictions provide similar data feeds, and there are even convenient assemblages of that data.
Meanwhile, large language models like the one underlying ChatGPT are routinely used for summarizing long, complex documents (even laws and computer code) to capture the essential points, and they are optimized to match human expectations. This capability could allow an AI assistant to automatically predict how detectable the true effect of a policy insertion may be to a human reader.
Today, it can take a highly paid team of human lobbyists days or weeks to generate and analyze alternative pieces of microlegislation on behalf of a client. With AI assistance, that could be done instantaneously and cheaply. This opens the door to dramatic increases in the scope of this kind of microlegislating, with a potential to scale across any number of bills in any jurisdiction.
Impact assessment is more complicated. There is a rich series of methods for quantifying the predicted outcome of a decision or policy, and then also optimizing the return under that model. This kind of approach goes by different names in different circles—mathematical programming in management science, utility maximization in economics, and rational design in the life sciences.
To train an AI to do this, we would need to specify some way to calculate the benefit to different parties as a result of a policy choice. That could mean estimating the financial return to different companies under a few different scenarios of taxation or regulation. Economists are skilled at building risk models like this, and companies are already required to formulate and disclose regulatory compliance risk factors to investors. Such a mathematical model could translate directly into a reward function, a grading system that could provide feedback for the model used to create policy proposals and direct the process of training it.
The real challenge in impact assessment for generative AI models would be to parse the textual output of a model like ChatGPT in terms that an economic model could readily use. Automating this would require extracting structured financial information from the draft amendment or any legalese surrounding it. This kind of information extraction, too, is an area where AI has a long history; for example, AI systems have been trained to recognize clinical details in doctors’ notes. Early indications are that large language models are fairly good at recognizing financial information in texts such as investor call transcripts. While it remains an open challenge in the field, they may even be capable of writing out multi-step plans based on descriptions in free text.
The last piece of the puzzle is a lobbying strategizer to figure out what actions to take to convince lawmakers to adopt the amendment.
Passing legislation requires a keen understanding of the complex interrelated networks of legislative offices, outside groups, executive agencies, and other stakeholders vying to serve their own interests. Each actor in this network has a baseline perspective and different factors that influence that point of view. For example, a legislator may be moved by seeing an allied stakeholder take a firm position, or by a negative news story, or by a campaign contribution.
It turns out that AI developers are very experienced at modeling these kinds of networks. Machine-learning models for network graphs have been built, refined, improved, and iterated by hundreds of researchers working on incredibly diverse problems: lidar scans used to guide self-driving cars, the chemical functions of molecular structures, the capture of motion in actors’ joints for computer graphics, behaviors in social networks, and more.
In the context of AI-assisted lobbying, political actors like legislators and lobbyists are nodes on a graph, just like users in a social network. Relations between them are graph edges, like social connections. Information can be passed along those edges, like messages sent to a friend or campaign contributions made to a member. AI models can use past examples to learn to estimate how that information changes the network. Calculating the likelihood that a campaign contribution of a given size will flip a legislator’s vote on an amendment is one application.
McKay’s work has already shown us that there are significant, predictable relationships between these actions and the outcomes of legislation, and that the work of discovering those can be automated. Others have shown that graphs of neural network models like those described above can be applied to political systems. The full-scale use of these technologies to guide lobbying strategy is theoretical, but plausible.
Put together, these three components could create an automatic system for generating profitable microlegislation. The policy proposal system would create millions, even billions, of possible amendments. The impact assessor would identify the few that promise to be most profitable to the client. And the lobbying strategy tool would produce a blueprint for getting them passed.
What remains is for human lobbyists to walk the floors of the Capitol or state house, and perhaps supply some cash to grease the wheels. These final two aspects of lobbying—access and financing—cannot be supplied by the AI tools we envision. This suggests that lobbying will continue to primarily benefit those who are already influential and wealthy, and AI assistance will amplify their existing advantages.
The transformative benefit that AI offers to lobbyists and their clients is scale. While individual lobbyists tend to focus on the federal level or a single state, with AI assistance they could more easily infiltrate a large number of state-level (or even local-level) law-making bodies and elections. At that level, where the average cost of a seat is measured in the tens of thousands of dollars instead of millions, a single donor can wield a lot of influence—if automation makes it possible to coordinate lobbying across districts.
When it comes to combating the potentially adverse effects of assistive AI, the first response always seems to be to try to detect whether or not content was AI-generated. We could imagine a defensive AI that detects anomalous lobbyist spending associated with amendments that benefit the contributing group. But by then, the damage might already be done.
In general, methods for detecting the work of AI tend not to keep pace with its ability to generate convincing content. And these strategies won’t be implemented by AIs alone. The lobbyists will still be humans who take the results of an AI microlegislator and further refine the computer’s strategies. These hybrid human-AI systems will not be detectable from their output.
But the good news is: the same strategies that have long been used to combat misbehavior by human lobbyists can still be effective when those lobbyists get an AI assist. We don’t need to reinvent our democracy to stave off the worst risks of AI; we just need to more fully implement long-standing ideals.
First, we should reduce the dependence of legislatures on monolithic, multi-thousand-page omnibus bills voted on under deadline. This style of legislating exploded in the 1980s and 1990s and continues through to the most recent federal budget bill. Notwithstanding their legitimate benefits to the political system, omnibus bills present an obvious and proven vehicle for inserting unnoticed provisions that may later surprise the same legislators who approved them.
The issue is not that individual legislators need more time to read and understand each bill (that isn’t realistic or even necessary). It’s that omnibus bills must pass. There is an imperative to pass a federal budget bill, and so the capacity to push back on individual provisions that may seem deleterious (or just impertinent) to any particular group is small. Bills that are too big to fail are ripe for hacking by microlegislation.
Moreover, the incentive for legislators to introduce microlegislation catering to a narrow interest is greater if the threat of exposure is lower. To strengthen the threat of exposure for misbehaving legislative sponsors, bills should focus more tightly on individual substantive areas and, after the introduction of amendments, allow more time before the committee and floor votes. During this time, we should encourage public review and testimony to provide greater oversight.
Second, we should strengthen disclosure requirements on lobbyists, whether they’re entirely human or AI-assisted. State laws regarding lobbying disclosure are a hodgepodge. North Dakota, for example, only requires lobbying reports to be filed annually, so that by the time a disclosure is made, the policy is likely already decided. A lobbying disclosure scorecard created by Open Secrets, a group researching the influence of money in US politics, tracks nine states that do not even require lobbyists to report their compensation.
Ideally, it would be great for the public to see all communication between lobbyists and legislators, whether it takes the form of a proposed amendment or not. Absent that, let’s give the public the benefit of reviewing what lobbyists are lobbying for—and why. Lobbying is traditionally an activity that happens behind closed doors. Right now, many states reinforce that: they actually exempt testimony delivered publicly to a legislature from being reported as lobbying.
In those jurisdictions, if you reveal your position to the public, you’re no longer lobbying. Let’s do the inverse: require lobbyists to reveal their positions on issues. Some jurisdictions already require a statement of position (a ‘yea’ or ‘nay’) from registered lobbyists. And in most (but not all) states, you could make a public records request regarding meetings held with a state legislator and hope to get something substantive back. But we can expect more—lobbyists could be required to proactively publish, within a few days, a brief summary of what they demanded of policymakers during meetings and why they believe it’s in the general interest.
We can’t rely on corporations to be forthcoming and wholly honest about the reasons behind their lobbying positions. But having them on the record about their intentions would at least provide a baseline for accountability.
Finally, consider the role AI assistive technologies may have on lobbying firms themselves and the labor market for lobbyists. Many observers are rightfully concerned about the possibility of AI replacing or devaluing the human labor it automates. If the automating potential of AI ends up commodifying the work of political strategizing and message development, it may indeed put some professionals on K Street out of work.
But don’t expect that to disrupt the careers of the most astronomically compensated lobbyists: former members Congress and other insiders who have passed through the revolving door. There is no shortage of reform ideas for limiting the ability of government officials turned lobbyists to sell access to their colleagues still in government, and they should be adopted and—equally important—maintained and enforced in successive Congresses and administrations.
None of these solutions are really original, specific to the threats posed by AI, or even predominantly focused on microlegislation—and that’s the point. Good governance should and can be robust to threats from a variety of techniques and actors.
But what makes the risks posed by AI especially pressing now is how fast the field is developing. We expect the scale, strategies, and effectiveness of humans engaged in lobbying to evolve over years and decades. Advancements in AI, meanwhile, seem to be making impressive breakthroughs at a much faster pace—and it’s still accelerating.
The legislative process is a constant struggle between parties trying to control the rules of our society as they are updated, rewritten, and expanded at the federal, state, and local levels. Lobbying is an important tool for balancing various interests through our system. If it’s well-regulated, perhaps lobbying can support policymakers in making equitable decisions on behalf of us all.
This article was co-written with Nathan E. Sanders and originally appeared in MIT Technology Review.
Sidebar photo of Bruce Schneier by Joe MacInnis.