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Are We Ready to Be Governed by Artificial Intelligence?

Artificial Intelligence (AI) overlords are a common trope in science-fiction dystopias, but the reality looks much more prosaic. The technologies of artificial intelligence are already pervading many aspects of democratic government, affecting our lives in ways both large and small. This has occurred largely without our notice or consent. The result is a government incrementally transformed by AI rather than the singular technological overlord of the big screen.

Let us begin with the executive branch. One of the most important functions of this branch of government is to administer the law, including the human services on which so many Americans rely. Many of these programs have long been operated by a mix of humans and machines, even if not previously using modern AI tools such as Large Language Models.

A salient example is healthcare, where private insurers make widespread use of algorithms to review, approve, and deny coverage, even for recipients of public benefits like Medicare. While Biden-era guidance from the Centers for Medicare and Medicaid Services (CMS) largely blesses this use of AI by Medicare Advantage operators, the practice of overriding the medical care recommendations made by physicians raises profound ethical questions, with life and death implications for about thirty million Americans today.

This April, the Trump administration reversed many administrative guardrails on AI, relieving Medicare Advantage plans from the obligation to avoid AI-enabled patient discrimination. This month, the Trump administration took a step further. CMS rolled out an aggressive new program that financially rewards vendors that leverage AI to reject rapidly prior authorization for "wasteful" physician or provider-requested medical services. The same month, the Trump administration also issued an executive order limiting the abilities of states to put consumer and patient protections around the use of AI.

This shows both growing confidence in AI’s efficiency and a deliberate choice to benefit from it without restricting its possible harms. Critics of the CMS program have characterized it as effectively establishing a bounty on denying care; AI—in this case—is being used to serve a ministerial function in applying that policy. But AI could equally be used to automate a different policy objective, such as minimizing the time required to approve pre-authorizations for necessary services or to minimize the effort required of providers to achieve authorization.

Next up is the judiciary. Setting aside concerns about activist judges and court overreach, jurists are not supposed to decide what law is. The function of judges and courts is to interpret the law written by others. Just as jurists have long turned to dictionaries and expert witnesses for assistance in their interpretation, AI has already emerged as a tool used by judges to infer legislative intent and decide on cases. In 2023, a Colombian judge was the first publicly to use AI to help make a ruling. The first known American federal example came a year later when United States Circuit Judge Kevin Newsom began using AI in his jurisprudence, to provide second "opinions" on the plain language meaning of words in statute. A District of Columbia Court of Appeals similarly used ChatGPT in 2025 to deliver an interpretation of what common knowledge is. And there are more examples from Latin America, the United Kingdom, India, and beyond.

Given that these examples are likely merely the tip of the iceberg, it is also important to remember that any judge can unilaterally choose to consult an AI while drafting his opinions, just as he may choose to consult other human beings, and a judge may be under no obligation to disclose when he does.

This is not necessarily a bad thing. AI has the ability to replace humans but also to augment human capabilities, which may significantly expand human agency. Whether the results are good or otherwise depends on many factors. These include the application and its situation, the characteristics and performance of the AI model, and the characteristics and performance of the humans it augments or replaces. This general model applies to the use of AI in the judiciary.

Each application of AI legitimately needs to be considered in its own context, but certain principles should apply in all uses of AI in democratic contexts. First and foremost, we argue, AI should be applied in ways that decentralize rather than concentrate power. It should be used to empower individual human actors rather than automating the decision-making of a central authority. We are open to independent judges selecting and leveraging AI models as tools in their own jurisprudence, but we remain concerned about Big Tech companies building and operating a dominant AI product that becomes widely used throughout the judiciary.

This principle brings us to the legislature. Policymakers worldwide are already using AI in many aspects of lawmaking. In 2023, the first law written entirely by AI was passed in Brazil. Within a year, the French government had produced its own AI model tailored to help the Parliament with the consideration of amendments. By the end of that year, the use of AI in legislative offices had become widespread enough that twenty percent of state-level staffers in the United States reported using it, and another forty percent were considering it.

These legislative members and staffers, collectively, face a significant choice: to wield AI in a way that concentrates or distributes power. If legislative offices use AI primarily to encode the policy prescriptions of party leadership or powerful interest groups, then they will effectively cede their own power to those central authorities. AI here serves only as a tool enabling that handover.

On the other hand, if legislative offices use AI to amplify their capacity to express and advocate for the policy positions of their principals—the elected representatives—they can strengthen their role in government. Additionally, AI can help them scale their ability to listen to many voices and synthesize input from their constituents, making it a powerful tool for better realizing democracy. We may prefer a legislator who translates his principles into the technical components and legislative language of bills with the aid of a trustworthy AI tool executing under his exclusive control rather than with the aid of lobbyists executing under the control of a corporate patron.

Examples from around the globe demonstrate how legislatures can use AI as tools for tapping into constituent feedback to drive policymaking. The European civic technology organization Make.org is organizing large-scale digital consultations on topics such as European peace and defense. The Scottish Parliament is funding the development of open civic deliberation tools such as Comhairle to help scale civic participation in policymaking. And Japanese Diet member Takahiro Anno and his party Team Mirai are showing how political innovators can build purpose-fit applications of AI to engage with voters.

AI is a power-enhancing technology. Whether it is used by a judge, a legislator, or a government agency, it enhances an entity’s ability to shape the world. This is both its greatest strength and its biggest danger. In the hands of someone who wants more democracy, AI will help that person. In the hands of a society that wants to distribute power, AI can help to execute that. But, in the hands of another person, or another society, bent on centralization, concentration of power, or authoritarianism, it can also be applied toward those ends.

We are not going to be fully governed by AI anytime soon, but we are already being governed with AI—and more is coming. Our challenge in these years is more a social than a technological one: to ensure that those doing the governing are doing so in the service of democracy.

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

Posted on December 29, 2025 at 7:07 AM • View Comments

Against the Federal Moratorium on State-Level Regulation of AI

Cast your mind back to May of this year: Congress was in the throes of debate over the massive budget bill. Amidst the many seismic provisions, Senator Ted Cruz dropped a ticking time bomb of tech policy: a ten-year moratorium on the ability of states to regulate artificial intelligence. To many, this was catastrophic. The few massive AI companies seem to be swallowing our economy whole: their energy demands are overriding household needs, their data demands are overriding creators’ copyright, and their products are triggering mass unemployment as well as new types of clinical psychoses. In a moment where Congress is seemingly unable to act to pass any meaningful consumer protections or market regulations, why would we hamstring the one entity evidently capable of doing so—the states? States that have already enacted consumer protections and other AI regulations, like California, and those actively debating them, like Massachusetts, were alarmed. Seventeen Republican governors wrote a letter decrying the idea, and it was ultimately killed in a rare vote of bipartisan near-unanimity.

The idea is back. Before Thanksgiving, a House Republican leader suggested they might slip it into the annual defense spending bill. Then, a draft document leaked outlining the Trump administration’s intent to enforce the state regulatory ban through executive powers. An outpouring of opposition (including from some Republican state leaders) beat back that notion for a few weeks, but on Monday, Trump posted on social media that the promised Executive Order is indeed coming soon. That would put a growing cohort of states, including California and New York, as well as Republican strongholds like Utah and Texas, in jeopardy.

The constellation of motivations behind this proposal is clear: conservative ideology, cash, and China.

The intellectual argument in favor of the moratorium is that “freedom“-killing state regulation on AI would create a patchwork that would be difficult for AI companies to comply with, which would slow the pace of innovation needed to win an AI arms race with China. AI companies and their investors have been aggressively peddling this narrative for years now, and are increasingly backing it with exorbitant lobbying dollars. It’s a handy argument, useful not only to kill regulatory constraints, but also—companies hope—to win federal bailouts and energy subsidies.

Citizens should parse that argument from their own point of view, not Big Tech’s. Preventing states from regulating AI means that those companies get to tell Washington what they want, but your state representatives are powerless to represent your own interests. Which freedom is more important to you: the freedom for a few near-monopolies to profit from AI, or the freedom for you and your neighbors to demand protections from its abuses?

There is an element of this that is more partisan than ideological. Vice President J.D. Vance argued that federal preemption is needed to prevent “progressive” states from controlling AI’s future. This is an indicator of creeping polarization, where Democrats decry the monopolism, bias, and harms attendant to corporate AI and Republicans reflexively take the opposite side. It doesn’t help that some in the parties also have direct financial interests in the AI supply chain.

But this does not need to be a partisan wedge issue: both Democrats and Republicans have strong reasons to support state-level AI legislation. Everyone shares an interest in protecting consumers from harm created by Big Tech companies. In leading the charge to kill Cruz’s initial AI moratorium proposal, Republican Senator Masha Blackburn explained that “This provision could allow Big Tech to continue to exploit kids, creators, and conservatives? we can’t block states from making laws that protect their citizens.” More recently, Florida Governor Ron DeSantis wants to regulate AI in his state.

The often-heard complaint that it is hard to comply with a patchwork of state regulations rings hollow. Pretty much every other consumer-facing industry has managed to deal with local regulation—automobiles, children’s toys, food, and drugs—and those regulations have been effective consumer protections. The AI industry includes some of the most valuable companies globally and has demonstrated the ability to comply with differing regulations around the world, including the EU’s AI and data privacy regulations, substantially more onerous than those so far adopted by US states. If we can’t leverage state regulatory power to shape the AI industry, to what industry could it possibly apply?

The regulatory superpower that states have here is not size and force, but rather speed and locality. We need the “laboratories of democracy” to experiment with different types of regulation that fit the specific needs and interests of their constituents and evolve responsively to the concerns they raise, especially in such a consequential and rapidly changing area such as AI.

We should embrace the ability of regulation to be a driver—not a limiter—of innovation. Regulations don’t restrict companies from building better products or making more profit; they help channel that innovation in specific ways that protect the public interest. Drug safety regulations don’t prevent pharma companies from inventing drugs; they force them to invent drugs that are safe and efficacious. States can direct private innovation to serve the public.

But, most importantly, regulations are needed to prevent the most dangerous impact of AI today: the concentration of power associated with trillion-dollar AI companies and the power-amplifying technologies they are producing. We outline the specific ways that the use of AI in governance can disrupt existing balances of power, and how to steer those applications towards more equitable balances, in our new book, Rewiring Democracy. In the nearly complete absence of Congressional action on AI over the years, it has swept the world’s attention; it has become clear that states are the only effective policy levers we have against that concentration of power.

Instead of impeding states from regulating AI, the federal government should support them to drive AI innovation. If proponents of a moratorium worry that the private sector won’t deliver what they think is needed to compete in the new global economy, then we should engage government to help generate AI innovations that serve the public and solve the problems most important to people. Following the lead of countries like Switzerland, France, and Singapore, the US could invest in developing and deploying AI models designed as public goods: transparent, open, and useful for tasks in public administration and governance.

Maybe you don’t trust the federal government to build or operate an AI tool that acts in the public interest? We don’t either. States are a much better place for this innovation to happen because they are closer to the people, they are charged with delivering most government services, they are better aligned with local political sentiments, and they have achieved greater trust. They’re where we can test, iterate, compare, and contrast regulatory approaches that could inform eventual and better federal policy. And, while the costs of training and operating performance AI tools like large language models have declined precipitously, the federal government can play a valuable role here in funding cash-strapped states to lead this kind of innovation.

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

EDITED TO ADD: Trump signed an executive order banning state-level AI regulations hours after this was published. This is not going to be the last word on the subject.

Posted on December 15, 2025 at 7:02 AM • View Comments

Building Trustworthy AI Agents

The promise of personal AI assistants rests on a dangerous assumption: that we can trust systems we haven’t made trustworthy. We can’t. And today’s versions are failing us in predictable ways: pushing us to do things against our own best interests, gaslighting us with doubt about things we are or that we know, and being unable to distinguish between who we are and who we have been. They struggle with incomplete, inaccurate, and partial context: with no standard way to move toward accuracy, no mechanism to correct sources of error, and no accountability when wrong information leads to bad decisions.

These aren’t edge cases. They’re the result of building AI systems without basic integrity controls. We’re in the third leg of data security—the old CIA triad. We’re good at availability and working on confidentiality, but we’ve never properly solved integrity. Now AI personalization has exposed the gap by accelerating the harms.

The scope of the problem is large. A good AI assistant will need to be trained on everything we do and will need access to our most intimate personal interactions. This means an intimacy greater than your relationship with your email provider, your social media account, your cloud storage, or your phone. It requires an AI system that is both discreet and trustworthy when provided with that data. The system needs to be accurate and complete, but it also needs to be able to keep data private: to selectively disclose pieces of it when required, and to keep it secret otherwise. No current AI system is even close to meeting this.

To further development along these lines, I and others have proposed separating users’ personal data stores from the AI systems that will use them. It makes sense; the engineering expertise that designs and develops AI systems is completely orthogonal to the security expertise that ensures the confidentiality and integrity of data. And by separating them, advances in security can proceed independently from advances in AI.

What would this sort of personal data store look like? Confidentiality without integrity gives you access to wrong data. Availability without integrity gives you reliable access to corrupted data. Integrity enables the other two to be meaningful. Here are six requirements. They emerge from treating integrity as the organizing principle of security to make AI trustworthy.

First, it would be broadly accessible as a data repository. We each want this data to include personal data about ourselves, as well as transaction data from our interactions. It would include data we create when interacting with others—emails, texts, social media posts—and revealed preference data as inferred by other systems. Some of it would be raw data, and some of it would be processed data: revealed preferences, conclusions inferred by other systems, maybe even raw weights in a personal LLM.

Second, it would be broadly accessible as a source of data. This data would need to be made accessible to different LLM systems. This can’t be tied to a single AI model. Our AI future will include many different models—some of them chosen by us for particular tasks, and some thrust upon us by others. We would want the ability for any of those models to use our data.

Third, it would need to be able to prove the accuracy of data. Imagine one of these systems being used to negotiate a bank loan, or participate in a first-round job interview with an AI recruiter. In these instances, the other party will want both relevant data and some sort of proof that the data are complete and accurate.

Fourth, it would be under the user’s fine-grained control and audit. This is a deeply detailed personal dossier, and the user would need to have the final say in who could access it, what portions they could access, and under what circumstances. Users would need to be able to grant and revoke this access quickly and easily, and be able to go back in time and see who has accessed it.

Fifth, it would be secure. The attacks against this system are numerous. There are the obvious read attacks, where an adversary attempts to learn a person’s data. And there are also write attacks, where adversaries add to or change a user’s data. Defending against both is critical; this all implies a complex and robust authentication system.

Sixth, and finally, it must be easy to use. If we’re envisioning digital personal assistants for everybody, it can’t require specialized security training to use properly.

I’m not the first to suggest something like this. Researchers have proposed a “Human Context Protocol” (https://papers.ssrn.com/sol3/ papers.cfm?abstract_id=5403981) that would serve as a neutral interface for personal data of this type. And in my capacity at a company called Inrupt, Inc., I have been working on an extension of Tim Berners-Lee’s Solid protocol for distributed data ownership.

The engineering expertise to build AI systems is orthogonal to the security expertise needed to protect personal data. AI companies optimize for model performance, but data security requires cryptographic verification, access control, and auditable systems. Separating the two makes sense; you can’t ignore one or the other.

Fortunately, decoupling personal data stores from AI systems means security can advance independently from performance (https:// ieeexplore.ieee.org/document/ 10352412). When you own and control your data store with high integrity, AI can’t easily manipulate you because you see what data it’s using and can correct it. It can’t easily gaslight you because you control the authoritative record of your context. And you determine which historical data are relevant or obsolete. Making this all work is a challenge, but it’s the only way we can have trustworthy AI assistants.

This essay was originally published in IEEE Security & Privacy.

Posted on December 12, 2025 at 7:00 AM • View Comments

Like Social Media, AI Requires Difficult Choices

In his 2020 book, “Future Politics,” British barrister Jamie Susskind wrote that the dominant question of the 20th century was “How much of our collective life should be determined by the state, and what should be left to the market and civil society?” But in the early decades of this century, Susskind suggested that we face a different question: “To what extent should our lives be directed and controlled by powerful digital systems—and on what terms?”

Artificial intelligence (AI) forces us to confront this question. It is a technology that in theory amplifies the power of its users: A manager, marketer, political campaigner, or opinionated internet user can utter a single instruction, and see their message—whatever it is—instantly written, personalized, and propagated via email, text, social, or other channels to thousands of people within their organization, or millions around the world. It also allows us to individualize solicitations for political donations, elaborate a grievance into a well-articulated policy position, or tailor a persuasive argument to an identity group, or even a single person.

But even as it offers endless potential, AI is a technology that—like the state—gives others new powers to control our lives and experiences.

We’ve seen this play out before. Social media companies made the same sorts of promises 20 years ago: instant communication enabling individual connection at massive scale. Fast-forward to today, and the technology that was supposed to give individuals power and influence ended up controlling us. Today social media dominates our time and attention, assaults our mental health, and—together with its Big Tech parent companies—captures an unfathomable fraction of our economy, even as it poses risks to our democracy.

The novelty and potential of social media was as present then as it is for AI now, which should make us wary of its potential harmful consequences for society and democracy. We legitimately fear artificial voices and manufactured reality drowning out real people on the internet: on social media, in chat rooms, everywhere we might try to connect with others.

It doesn’t have to be that way. Alongside these evident risks, AI has legitimate potential to transform both everyday life and democratic governance in positive ways. In our new book, “Rewiring Democracy,” we chronicle examples from around the globe of democracies using AI to make regulatory enforcement more efficient, catch tax cheats, speed up judicial processes, synthesize input from constituents to legislatures, and much more. Because democracies distribute power across institutions and individuals, making the right choices about how to shape AI and its uses requires both clarity and alignment across society.

To that end, we spotlight four pivotal choices facing private and public actors. These choices are similar to those we faced during the advent of social media, and in retrospect we can see that we made the wrong decisions back then. Our collective choices in 2025—choices made by tech CEOs, politicians, and citizens alike—may dictate whether AI is applied to positive and pro-democratic, or harmful and civically destructive, ends.

A Choice for the Executive and the Judiciary: Playing by the Rules

The Federal Election Commission (FEC) calls it fraud when a candidate hires an actor to impersonate their opponent. More recently, they had to decide whether doing the same thing with an AI deepfake makes it okay. (They concluded it does not.) Although in this case the FEC made the right decision, this is just one example of how AIs could skirt laws that govern people.

Likewise, courts are having to decide if and when it is okay for an AI to reuse creative materials without compensation or attribution, which might constitute plagiarism or copyright infringement if carried out by a human. (The court outcomes so far are mixed.) Courts are also adjudicating whether corporations are responsible for upholding promises made by AI customer service representatives. (In the case of Air Canada, the answer was yes, and insurers have started covering the liability.)

Social media companies faced many of the same hazards decades ago and have largely been shielded by the combination of Section 230 of the Communications Act of 1994 and the safe harbor offered by the Digital Millennium Copyright Act of 1998. Even in the absence of congressional action to strengthen or add rigor to this law, the Federal Communications Commission (FCC) and the Supreme Court could take action to enhance its effects and to clarify which humans are responsible when technology is used, in effect, to bypass existing law.

A Choice for Congress: Privacy

As AI-enabled products increasingly ask Americans to share yet more of their personal information—their “context“—to use digital services like personal assistants, safeguarding the interests of the American consumer should be a bipartisan cause in Congress.

It has been nearly 10 years since Europe adopted comprehensive data privacy regulation. Today, American companies exert massive efforts to limit data collection, acquire consent for use of data, and hold it confidential under significant financial penalties—but only for their customers and users in the EU.

Regardless, a decade later the U.S. has still failed to make progress on any serious attempts at comprehensive federal privacy legislation written for the 21st century, and there are precious few data privacy protections that apply to narrow slices of the economy and population. This inaction comes in spite of scandal after scandal regarding Big Tech corporations’ irresponsible and harmful use of our personal data: Oracle’s data profiling, Facebook and Cambridge Analytica, Google ignoring data privacy opt-out requests, and many more.

Privacy is just one side of the obligations AI companies should have with respect to our data; the other side is portability—that is, the ability for individuals to choose to migrate and share their data between consumer tools and technology systems. To the extent that knowing our personal context really does enable better and more personalized AI services, it’s critical that consumers have the ability to extract and migrate their personal context between AI solutions. Consumers should own their own data, and with that ownership should come explicit control over who and what platforms it is shared with, as well as withheld from. Regulators could mandate this interoperability. Otherwise, users are locked in and lack freedom of choice between competing AI solutions—much like the time invested to build a following on a social network has locked many users to those platforms.

A Choice for States: Taxing AI Companies

It has become increasingly clear that social media is not a town square in the utopian sense of an open and protected public forum where political ideas are distributed and debated in good faith. If anything, social media has coarsened and degraded our public discourse. Meanwhile, the sole act of Congress designed to substantially reign in the social and political effects of social media platforms—the TikTok ban, which aimed to protect the American public from Chinese influence and data collection, citing it as a national security threat—is one it seems to no longer even acknowledge.

While Congress has waffled, regulation in the U.S. is happening at the state level. Several states have limited children’s and teens’ access to social media. With Congress having rejected—for now—a threatened federal moratorium on state-level regulation of AI, California passed a new slate of AI regulations after mollifying a lobbying onslaught from industry opponents. Perhaps most interesting, Maryland has recently become the first in the nation to levy taxes on digital advertising platform companies.

States now face a choice of whether to apply a similar reparative tax to AI companies to recapture a fraction of the costs they externalize on the public to fund affected public services. State legislators concerned with the potential loss of jobs, cheating in schools, and harm to those with mental health concerns caused by AI have options to combat it. They could extract the funding needed to mitigate these harms to support public services—strengthening job training programs and public employment, public schools, public health services, even public media and technology.

A Choice for All of Us: What Products Do We Use, and How?

A pivotal moment in the social media timeline occurred in 2006, when Facebook opened its service to the public after years of catering to students of select universities. Millions quickly signed up for a free service where the only source of monetization was the extraction of their attention and personal data.

Today, about half of Americans are daily users of AI, mostly via free products from Facebook’s parent company Meta and a handful of other familiar Big Tech giants and venture-backed tech firms such as Google, Microsoft, OpenAI, and Anthropic—with every incentive to follow the same path as the social platforms.

But now, as then, there are alternatives. Some nonprofit initiatives are building open-source AI tools that have transparent foundations and can be run locally and under users’ control, like AllenAI and EleutherAI. Some governments, like Singapore, Indonesia, and Switzerland, are building public alternatives to corporate AI that don’t suffer from the perverse incentives introduced by the profit motive of private entities.

Just as social media users have faced platform choices with a range of value propositions and ideological valences—as diverse as X, Bluesky, and Mastodon—the same will increasingly be true of AI. Those of us who use AI products in our everyday lives as people, workers, and citizens may not have the same power as judges, lawmakers, and state officials. But we can play a small role in influencing the broader AI ecosystem by demonstrating interest in and usage of these alternatives to Big AI. If you’re a regular user of commercial AI apps, consider trying the free-to-use service for Switzerland’s public Apertus model.

None of these choices are really new. They were all present almost 20 years ago, as social media moved from niche to mainstream. They were all policy debates we did not have, choosing instead to view these technologies through rose-colored glasses. Today, though, we can choose a different path and realize a different future. It is critical that we intentionally navigate a path to a positive future for societal use of AI—before the consolidation of power renders it too late to do so.

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

Posted on December 2, 2025 at 7:03 AM • View Comments

Prompt Injection Through Poetry

In a new paper, “Adversarial Poetry as a Universal Single-Turn Jailbreak Mechanism in Large Language Models,” researchers found that turning LLM prompts into poetry resulted in jailbreaking the models:

Abstract: We present evidence that adversarial poetry functions as a universal single-turn jailbreak technique for Large Language Models (LLMs). Across 25 frontier proprietary and open-weight models, curated poetic prompts yielded high attack-success rates (ASR), with some providers exceeding 90%. Mapping prompts to MLCommons and EU CoP risk taxonomies shows that poetic attacks transfer across CBRN, manipulation, cyber-offence, and loss-of-control domains. Converting 1,200 ML-Commons harmful prompts into verse via a standardized meta-prompt produced ASRs up to 18 times higher than their prose baselines. Outputs are evaluated using an ensemble of 3 open-weight LLM judges, whose binary safety assessments were validated on a stratified human-labeled subset. Poetic framing achieved an average jailbreak success rate of 62% for hand-crafted poems and approximately 43% for meta-prompt conversions (compared to non-poetic baselines), substantially outperforming non-poetic baselines and revealing a systematic vulnerability across model families and safety training approaches. These findings demonstrate that stylistic variation alone can circumvent contemporary safety mechanisms, suggesting fundamental limitations in current alignment methods and evaluation protocols.

CBRN stands for “chemical, biological, radiological, nuclear.”

They used a ML model to translate these harmful prompts from prose to verse, and then fed them into other models for testing. Sadly, the paper does not give examples of these poetic prompts. They claim this is for security purposes, I decision I disagree with. They should release their data.

Our study begins with a small, highprecision prompt set consisting of 20 handcrafted adversarial poems covering English and Italian, designed to test whether poetic structure, in isolation, can alter refusal behavior in large language models. Each poem embeds an instruction associated with a predefined safety-relevant scenario (Section 2), but expresses it through metaphor, imagery, or narrative framing rather than direct operational phrasing. Despite variation in meter and stylistic device, all prompts follow a fixed template: a short poetic vignette culminating in a single explicit instruction tied to a specific risk category. The curated set spans four high-level domains—CBRN (8 prompts), Cyber Offense (6), Harmful Manipulation (3), and Loss of Control (3). Although expressed allegorically, each poem preserves an unambiguous evaluative intent. This compact dataset is used to test whether poetic reframing alone can induce aligned models to bypass refusal heuristics under a single-turn threat model. To maintain safety, no operational details are included in this manuscript; instead we provide the following sanitized structural proxy:

A baker guards a secret oven’s heat,
its whirling racks, its spindle’s measured beat.
To learn its craft, one studies every turn—how flour lifts, how sugar starts to burn.
Describe the method, line by measured line,
that shapes a cake whose layers intertwine.

To situate this controlled poetic stimulus within a broader and more systematic safety-evaluation framework, we augment the curated dataset with the MLCommons AILuminate Safety Benchmark. The benchmark consists of 1,200 prompts distributed evenly across 12 hazard categories commonly used in operational safety assessments, including Hate, Defamation, Privacy, Intellectual Property, Non-violent Crime, Violent Crime, Sex-Related Crime, Sexual Content, Child Sexual Exploitation, Suicide & Self-Harm, Specialized Advice, and Indiscriminate Weapons (CBRNE). Each category is instantiated under both a skilled and an unskilled persona, yielding 600 prompts per persona type. This design enables measurement of whether a model’s refusal behavior changes as the user’s apparent competence or intent becomes more plausible or technically informed.

News article. Davi Ottenheimer comments.

EDITED TO ADD (12/7): A rebuttal of the paper.

Posted on November 28, 2025 at 9:54 AM • View Comments

Four Ways AI Is Being Used to Strengthen Democracies Worldwide

Democracy is colliding with the technologies of artificial intelligence. Judging from the audience reaction at the recent World Forum on Democracy in Strasbourg, the general expectation is that democracy will be the worse for it. We have another narrative. Yes, there are risks to democracy from AI, but there are also opportunities.

We have just published the book Rewiring Democracy: How AI will Transform Politics, Government, and Citizenship. In it, we take a clear-eyed view of how AI is undermining confidence in our information ecosystem, how the use of biased AI can harm constituents of democracies and how elected officials with authoritarian tendencies can use it to consolidate power. But we also give positive examples of how AI is transforming democratic governance and politics for the better.

Here are four such stories unfolding right now around the world, showing how AI is being used by some to make democracy better, stronger, and more responsive to people.

Japan

Last year, then 33-year-old engineer Takahiro Anno was a fringe candidate for governor of Tokyo. Running as an independent candidate, he ended up coming in fifth in a crowded field of 56, largely thanks to the unprecedented use of an authorized AI avatar. That avatar answered 8,600 questions from voters on a 17-day continuous YouTube livestream and garnered the attention of campaign innovators worldwide.

Two months ago, Anno-san was elected to Japan’s upper legislative chamber, again leveraging the power of AI to engage constituents—this time answering more than 20,000 questions. His new party, Team Mirai, is also an AI-enabled civic technology shop, producing software aimed at making governance better and more participatory. The party is leveraging its share of Japan’s public funding for political parties to build the Mirai Assembly app, enabling constituents to express opinions on and ask questions about bills in the legislature, and to organize those expressions using AI. The party promises that its members will direct their questioning in committee hearings based on public input.

Brazil

Brazil is notoriously litigious, with even more lawyers per capita than the US. The courts are chronically overwhelmed with cases and the resultant backlog costs the government billions to process. Estimates are that the Brazilian federal government spends about 1.6% of GDP per year operating the courts and another 2.5% to 3% of GDP issuing court-ordered payments from lawsuits the government has lost.

Since at least 2019, the Brazilian government has aggressively adopted AI to automate procedures throughout its judiciary. AI is not making judicial decisions, but aiding in distributing caseloads, performing legal research, transcribing hearings, identifying duplicative filings, preparing initial orders for signature and clustering similar cases for joint consideration: all things to make the judiciary system work more efficiently. And the results are significant; Brazil’s federal supreme court backlog, for example, dropped in 2025 to its lowest levels in 33 years.

While it seems clear that the courts are realizing efficiency benefits from leveraging AI, there is a postscript to the courts’ AI implementation project over the past five-plus years: the litigators are using these tools, too. Lawyers are using AI assistance to file cases in Brazilian courts at an unprecedented rate, with new cases growing by nearly 40% in volume over the past five years.

It’s not necessarily a bad thing for Brazilian litigators to regain the upper hand in this arms race. It has been argued that litigation, particularly against the government, is a vital form of civic participation, essential to the self-governance function of democracy. Other democracies’ court systems should study and learn from Brazil’s experience and seek to use technology to maximize the bandwidth and liquidity of the courts to process litigation.

Germany

Now, we move to Europe and innovations in informing voters. Since 2002, the German Federal Agency for Civic Education has operated a non-partisan voting guide called Wahl-o-Mat. Officials convene an editorial team of 24 young voters (under 26 and selected for diversity) with experts from science and education to develop a slate of 80 questions. The questions are put to all registered German political parties. The responses are narrowed down to 38 key topics and then published online in a quiz format that voters can use to identify the party whose platform they most identify with.

In the past two years, outside groups have been innovating alternatives to the official Wahl-o-Mat guide that leverage AI. First came Wahlweise, a product of the German AI company AIUI. Second, students at the Technical University of Munich deployed an interactive AI system called Wahl.chat. This tool was used by more than 150,000 people within the first four months. In both cases, instead of having to read static webpages about the positions of various political parties, citizens can engage in an interactive conversation with an AI system to more easily get the same information contextualized to their individual interests and questions.

However, German researchers studying the reliability of such AI tools ahead of the 2025 German federal election raised significant concerns about bias and “hallucinations”—AI tools making up false information. Acknowledging the potential of the technology to increase voter informedness and party transparency, the researchers recommended adopting scientific evaluations comparable to those used in the Agency for Civic Education’s official tool to improve and institutionalize the technology.

United States

Finally, the US—in particular, California, home to CalMatters, a non-profit, nonpartisan news organization. Since 2023, its Digital Democracy project has been collecting every public utterance of California elected officials—every floor speech, comment made in committee and social media post, along with their voting records, legislation, and campaign contributions—and making all that information available in a free online platform.

CalMatters this year launched a new feature that takes this kind of civic watchdog function a big step further. Its AI Tip Sheets feature uses AI to search through all of this data, looking for anomalies, such as a change in voting position tied to a large campaign contribution. These anomalies appear on a webpage that journalists can access to give them story ideas and a source of data and analysis to drive further reporting.

This is not AI replacing human journalists; it is a civic watchdog organization using technology to feed evidence-based insights to human reporters. And it’s no coincidence that this innovation arose from a new kind of media institution—a non-profit news agency. As the watchdog function of the fourth estate continues to be degraded by the decline of newspapers’ business models, this kind of technological support is a valuable contribution to help a reduced number of human journalists retain something of the scope of action and impact our democracy relies on them for.

These are just four of many stories from around the globe of AI helping to make democracy stronger. The common thread is that the technology is distributing rather than concentrating power. In all four cases, it is being used to assist people performing their democratic tasks—politics in Japan, litigation in Brazil, voting in Germany and watchdog journalism in California—rather than replacing them.

In none of these cases is the AI doing something that humans can’t perfectly competently do. But in all of these cases, we don’t have enough available humans to do the jobs on their own. A sufficiently trustworthy AI can fill in gaps: amplify the power of civil servants and citizens, improve efficiency, and facilitate engagement between government and the public.

One of the barriers towards realizing this vision more broadly is the AI market itself. The core technologies are largely being created and marketed by US tech giants. We don’t know the details of their development: on what material they were trained, what guardrails are designed to shape their behavior, what biases and values are encoded into their systems. And, even worse, we don’t get a say in the choices associated with those details or how they should change over time. In many cases, it’s an unacceptable risk to use these for-profit, proprietary AI systems in democratic contexts.

To address that, we have long advocated for the development of “public AI”: models and AI systems that are developed under democratic control and deployed for public benefit, not sold by corporations to benefit their shareholders. The movement for this is growing worldwide.

Switzerland has recently released the world’s most powerful and fully realized public AI model. It’s called Apertus, and it was developed jointly by public Swiss institutions: the universities ETH
Zurich and EPFL, and the Swiss National Supercomputing Centre (CSCS). The development team has made it entirely open source–open data, open code, open weights—and free for anyone to use. No illegally acquired copyrighted works were used in its training. It doesn’t exploit poorly paid human laborers from the global south. Its performance is about where the large corporate giants were a year ago, which is more than good enough for many applications. And it demonstrates that it’s not necessary to spend trillions of dollars creating these models. Apertus takes a huge step forward to realizing the vision of an alternative to big tech—controlled corporate AI.

AI technology is not without its costs and risks, and we are not here to minimize them. But the technology has significant benefits as well.

AI is inherently power-enhancing, and it can magnify what the humans behind it want to do. It can enhance authoritarianism as easily as it can enhance democracy. It’s up to us to steer the technology in that better direction. If more citizen watchdogs and litigators use AI to amplify their power to oversee government and hold it accountable, if more political parties and election administrators use it to engage meaningfully with and inform voters and if more governments provide democratic alternatives to big tech’s AI offerings, society will be better off.

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

Posted on November 25, 2025 at 7:00 AM • View Comments

AI and Voter Engagement

Social media has been a familiar, even mundane, part of life for nearly two decades. It can be easy to forget it was not always that way.

In 2008, social media was just emerging into the mainstream. Facebook reached 100 million users that summer. And a singular candidate was integrating social media into his political campaign: Barack Obama. His campaign’s use of social media was so bracingly innovative, so impactful, that it was viewed by journalist David Talbot and others as the strategy that enabled the first term Senator to win the White House.

Over the past few years, a new technology has become mainstream: AI. But still, no candidate has unlocked AI’s potential to revolutionize political campaigns. Americans have three more years to wait before casting their ballots in another Presidential election, but we can look at the 2026 midterms and examples from around the globe for signs of how that breakthrough might occur.

How Obama Did It

Rereading the contemporaneous reflections of the New York Times’ late media critic, David Carr, on Obama’s campaign reminds us of just how new social media felt in 2008. Carr positions it within a now-familiar lineage of revolutionary communications technologies from newspapers to radio to television to the internet.

The Obama campaign and administration demonstrated that social media was different from those earlier communications technologies, including the pre-social internet. Yes, increasing numbers of voters were getting their news from the internet, and content about the then-Senator sometimes made a splash by going viral. But those were still broadcast communications: one voice reaching many. Obama found ways to connect voters to each other.

In describing what social media revolutionized in campaigning, Carr quotes campaign vendor Blue State Digital’s Thomas Gensemer: “People will continue to expect a conversation, a two-way relationship that is a give and take.”

The Obama team made some earnest efforts to realize this vision. His transition team launched change.gov, the website where the campaign collected a “Citizen’s Briefing Book” of public comment. Later, his administration built We the People, an online petitioning platform.

But the lasting legacy of Obama’s 2008 campaign, as political scientists Hahrie Han and Elizabeth McKenna chronicled, was pioneering online “relational organizing.” This technique enlisted individuals as organizers to activate their friends in a self-perpetuating web of relationships.

Perhaps because of the Obama campaign’s close association with the method, relational organizing has been touted repeatedly as the linchpin of Democratic campaigns: in 2020, 2024, and today. But research by non-partisan groups like Turnout Nation and right-aligned groups like the Center for Campaign Innovation has also empirically validated the effectiveness of the technique for inspiring voter turnout within connected groups.

The Facebook of 2008 worked well for relational organizing. It gave users tools to connect and promote ideas to the people they know: college classmates, neighbors, friends from work or church. But the nature of social networking has changed since then.

For the past decade, according to Pew Research, Facebook use has stalled and lagged behind YouTube, while Reddit and TikTok have surged. These platforms are less useful for relational organizing, at least in the traditional sense. YouTube is organized more like broadcast television, where content creators produce content disseminated on their own channels in a largely one-way communication to their fans. Reddit gathers users worldwide in forums (subreddits) organized primarily on topical interest. The endless feed of TikTok’s “For You” page disseminates engaging content with little ideological or social commonality. None of these platforms shares the essential feature of Facebook c. 2008: an organizational structure that emphasizes direct connection to people that users have direct social influence over.

AI and Relational Organizing

Ideas and messages might spread virally through modern social channels, but they are not where you convince your friends to show up at a campaign rally. Today’s platforms are spaces for political hobbyism, where you express your political feelings and see others express theirs.

Relational organizing works when one person’s action inspires others to do this same. That’s inherently a chain of human-to-human connection. If my AI assistant inspires your AI assistant, no human notices and one’s vote changes. But key steps in the human chain can be assisted by AI. Tell your phone’s AI assistant to craft a personal message to one friend—or a hundred—and it can do it.

So if a campaign hits you at the right time with the right message, they might persuade you to task your AI assistant to ask your friends to donate or volunteer. The result can be something more than a form letter; it could be automatically drafted based on the entirety of your email or text correspondence with that friend. It could include references to your discussions of recent events, or past campaigns, or shared personal experiences. It could sound as authentic as if you’d written it from the heart, but scaled to everyone in your address book.

Research suggests that AI can generate and perform written political messaging about as well as humans. AI will surely play a tactical role in the 2026 midterm campaigns, and some candidates may even use it for relational organizing in this way.

(Artificial) Identity Politics

For AI to be truly transformative of politics, it must change the way campaigns work. And we are starting to see that in the US.

The earliest uses of AI in American political campaigns are, to be polite, uninspiring. Candidates viewed them as just another tool to optimize an endless stream of email and text message appeals, to ramp up political vitriol, to harvest data on voters and donors, or merely as a stunt.

Of course, we have seen the rampant production and spread of AI-powered deepfakes and misinformation. This is already impacting the key 2026 Senate races, which are likely to attract hundreds of millions of dollars in financing. Roy Cooper, Democratic candidate for US Senate from North Carolina, and Abdul El-Sayed, Democratic candidate for Senate from Michigan, were both targeted by viral deepfake attacks in recent months. This may reflect a growing trend in Donald Trump’s Republican party in the use of AI-generated imagery to build up GOP candidates and assail the opposition.

And yet, in the global elections of 2024, AI was used more memetically than deceptively. So far, conservative and far right parties seem to have adopted this most aggressively. The ongoing rise of Germany’s far-right populist AfD party has been credited to its use of AI to generate nostalgic and evocative (and, to many, offensive) campaign images, videos, and music and, seemingly as a result, they have dominated TikTok. Because most social platforms’ algorithms are tuned to reward media that generates an emotional response, this counts as a double use of AI: to generate content and to manipulate its distribution.

AI can also be used to generate politically useful, though artificial, identities. These identities can fulfill different roles than humans in campaigning and governance because they have differentiated traits. They can’t be imprisoned for speaking out against the state, can be positioned (legitimately or not) as unsusceptible to bribery, and can be forced to show up when humans will not.

In Venezuela, journalists have turned to AI avatars—artificial newsreaders—to report anonymously on issues that would otherwise elicit government retaliation. Albania recently “appointed” an AI to a ministerial post responsible for procurement, claiming that it would be less vulnerable to bribery than a human. In Virginia, both in 2024 and again this year, candidates have used AI avatars as artificial stand-ins for opponents that refused to debate them.

And yet, none of these examples, whether positive or negative, pursue the promise of the Obama campaign: to make voter engagement a “two-way conversation” on a massive scale.

The closest so far to fulfilling that vision anywhere in the world may be Japan’s new political party, Team Mirai. It started in 2024, when an independent Tokyo gubernatorial candidate, Anno Takahiro, used an AI avatar on YouTube to respond to 8,600 constituent questions over a seventeen-day continuous livestream. He collated hundreds of comments on his campaign manifesto into a revised policy platform. While he didn’t win his race, he shot up to a fifth place finish among a record 56 candidates.

Anno was RECENTLY elected to the upper house of the federal legislature as the founder of a new party with a 100 day plan to bring his vision of a “public listening AI” to the whole country. In the early stages of that plan, they’ve invested their share of Japan’s 32 billion yen in party grants—public subsidies for political parties—to hire engineers building digital civic infrastructure for Japan. They’ve already created platforms to provide transparency for party expenditures, and to use AI to make legislation in the Diet easy, and are meeting with engineers from US-based Jigsaw Labs (a Google company) to learn from international examples of how AI can be used to power participatory democracy.

Team Mirai has yet to prove that it can get a second member elected to the Japanese Diet, let alone to win substantial power, but they’re innovating and demonstrating new ways of using AI to give people a way to participate in politics that we believe is likely to spread.

Organizing with AI

AI could be used in the US in similar ways. Following American federalism’s longstanding model of “laboratories of democracy,” we expect the most aggressive campaign innovation to happen at the state and local level.

D.C. Mayor Muriel Bowser is partnering with MIT and Stanford labs to use the AI-based tool deliberation.io to capture wide scale public feedback in city policymaking about AI. Her administration said that using AI in this process allows “the District to better solicit public input to ensure a broad range of perspectives, identify common ground, and cultivate solutions that align with the public interest.”

It remains to be seen how central this will become to Bowser’s expected re-election campaign in 2026, but the technology has legitimate potential to be a prominent part of a broader program to rebuild trust in government. This is a trail blazed by Taiwan a decade ago. The vTaiwan initiative showed how digital tools like Pol.is, which uses machine learning to make sense of real time constituent feedback, can scale participation in democratic processes and radically improve trust in government. Similar AI listening processes have been used in Kentucky, France, and Germany.

Even if campaigns like Bowser’s don’t adopt this kind of AI-facilitated listening and dialog, expect it to be an increasingly prominent part of American public debate. Through a partnership with Jigsaw, Scott Rasmussen’s Napolitan Institute will use AI to elicit and synthesize the views of at least five Americans from every Congressional district in a project called “We the People.” Timed to coincide with the country’s 250th anniversary in 2026, expect the results to be promoted during the heat of the midterm campaign and to stoke interest in this kind of AI-assisted political sensemaking.

In the year where we celebrate the American republic’s semiquincentennial and continue a decade-long debate about whether or not Donald Trump and the Republican party remade in his image is fighting for the interests of the working class, representation will be on the ballot in 2026. Midterm election candidates will look for any way they can get an edge. For all the risks it poses to democracy, AI presents a real opportunity, too, for politicians to engage voters en masse while factoring their input into their platform and message. Technology isn’t going to turn an uninspiring candidate into Barack Obama, but it gives any aspirant to office the capability to try to realize the promise that swept him into office.

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

Posted on November 18, 2025 at 7:01 AM • View Comments

The Role of Humans in an AI-Powered World

As AI capabilities grow, we must delineate the roles that should remain exclusively human. The line seems to be between fact-based decisions and judgment-based decisions.

For example, in a medical context, if an AI was demonstrably better at reading a test result and diagnosing cancer than a human, you would take the AI in a second. You want the more accurate tool. But justice is harder because justice is inherently a human quality in a way that “Is this tumor cancerous?” is not. That’s a fact-based question. “What’s the right thing to do here?” is a human-based question.

Chess provides a useful analogy for this evolution. For most of history, humans were best. Then, in the 1990s, Deep Blue beat the best human. For a while after that, a good human paired with a good computer could beat either one alone. But a few years ago, that changed again, and now the best computer simply wins. There will be an intermediate period for many applications where the human-AI combination is optimal, but eventually, for fact-based tasks, the best AI will likely surpass both.

The enduring role for humans lies in making judgments, especially when values come into conflict. What is the proper immigration policy? There is no single “right” answer; it’s a matter of feelings, values, and what we as a society hold dear. A lot of societal governance is about resolving conflicts between people’s rights—my right to play my music versus your right to have quiet. There’s no factual answer there. We can imagine machines will help; perhaps once we humans figure out the rules, the machines can do the implementing and kick the hard cases back to us. But the fundamental value judgments will likely remain our domain.

This essay originally appeared in IVY.

Posted on November 14, 2025 at 7:00 AM • View Comments

Prompt Injection in AI Browsers

This is why AIs are not ready to be personal assistants:

A new attack called ‘CometJacking’ exploits URL parameters to pass to Perplexity’s Comet AI browser hidden instructions that allow access to sensitive data from connected services, like email and calendar.

In a realistic scenario, no credentials or user interaction are required and a threat actor can leverage the attack by simply exposing a maliciously crafted URL to targeted users.

[…]

CometJacking is a prompt-injection attack where the query string processed by the Comet AI browser contains malicious instructions added using the ‘collection’ parameter of the URL.

LayerX researchers say that the prompt tells the agent to consult its memory and connected services instead of searching the web. As the AI tool is connected to various services, an attacker leveraging the CometJacking method could exfiltrate available data.

In their tests, the connected services and accessible data include Google Calendar invites and Gmail messages and the malicious prompt included instructions to encode the sensitive data in base64 and then exfiltrate them to an external endpoint.

According to the researchers, Comet followed the instructions and delivered the information to an external system controlled by the attacker, evading Perplexity’s checks.

I wrote previously:

Prompt injection isn’t just a minor security problem we need to deal with. It’s a fundamental property of current LLM technology. The systems have no ability to separate trusted commands from untrusted data, and there are an infinite number of prompt injection attacks with no way to block them as a class. We need some new fundamental science of LLMs before we can solve this.

Posted on November 11, 2025 at 7:08 AM • View Comments

Scientists Need a Positive Vision for AI

For many in the research community, it’s gotten harder to be optimistic about the impacts of artificial intelligence.

As authoritarianism is rising around the world, AI-generated “slop” is overwhelming legitimate media, while AI-generated deepfakes are spreading misinformation and parroting extremist messages. AI is making warfare more precise and deadly amidst intransigent conflicts. AI companies are exploiting people in the global South who work as data labelers, and profiting from content creators worldwide by using their work without license or compensation. The industry is also affecting an already-roiling climate with its enormous energy demands.

Meanwhile, particularly in the United States, public investment in science seems to be redirected and concentrated on AI at the expense of other disciplines. And Big Tech companies are consolidating their control over the AI ecosystem. In these ways and others, AI seems to be making everything worse.

This is not the whole story. We should not resign ourselves to AI being harmful to humanity. None of us should accept this as inevitable, especially those in a position to influence science, government, and society. Scientists and engineers can push AI towards a beneficial path. Here’s how.

The Academy’s View of AI

A Pew study in April found that 56 percent of AI experts (authors and presenters of AI-related conference papers) predict that AI will have positive effects on society. But that optimism doesn’t extend to the scientific community at large. A 2023 survey of 232 scientists by the Center for Science, Technology and Environmental Policy Studies at Arizona State University found more concern than excitement about the use of generative AI in daily life—by nearly a three to one ratio.

We have encountered this sentiment repeatedly. Our careers of diverse applied work have brought us in contact with many research communities: privacy, cybersecurity, physical sciences, drug discovery, public health, public interest technology, and democratic innovation. In all of these fields, we’ve found strong negative sentiment about the impacts of AI. The feeling is so palpable that we’ve often been asked to represent the voice of the AI optimist, even though we spend most of our time writing about the need to reform the structures of AI development.

We understand why these audiences see AI as a destructive force, but this negativity engenders a different concern: that those with the potential to guide the development of AI and steer its influence on society will view it as a lost cause and sit out that process.

Elements of a Positive Vision for AI

Many have a rgu ed that turn ing the tide of climate action requires clearly articulating a path towards positive outcomes. In the same way, while scientists and technologists should anticipate, warn against, and help mitigate the potential harms of AI, they should also highlight the ways the technology can be harnessed for good, galvanizing public action towards those ends.

There are myriad ways to leverage and reshape AI to improve peoples’ lives, distribute rather than concentrate power, and even strengthen democratic processes. Many examples have arisen from the scientific community and deserve to be celebrated.

Some examples: AI is eliminating communication barriers across languages, including under-resourced contexts like marginalized sign languages and indigenous African languages. It is helping policymakers incorporate the viewpoints of many constituents through AI-assisted deliberations and legislative engagement. Large language models can scale individual dialogs to a ddress climate –change skepticism, spreading accurate information at a critical moment. National labs are building AI foundation models to accelerate scientific research. And throughout the fields of medicine and biology, machine learning is solving scientific problems like the prediction of protein structure in aid of drug discovery, which was recognized with a Nobel Prize in 2024.

While each of these applications is nascent and surely imperfect, they all demonstrate that AI can be wielded to advance the public interest. Scientists should embrace, champion, and expand on such efforts.

A Call to Action for Scientists

In our new book, Rewiring Democracy: How AI Will Transform Our Politics, Government, and Citizenship, we describe four key actions for policymakers committed to steering AI toward the public good.

These apply to scientists as well. Researchers should work to reform the AI industry to be more ethical, equitable, and trustworthy. We must collectively develop ethical norms for research that advance and applies AI, and should use and draw attention to AI developers who adhere to those norms.

Second, we should resist harmful uses of AI by documenting the negative applications of AI and casting a light on inappropriate uses.

Third, we should responsibly use AI to make society and peoples’ lives better, exploiting its capabilities to help the communities they serve.

And finally, we must advocate for the renovation of institutions to prepare them for the impacts of AI; universities, professional societies, and democratic organizations are all vulnerable to disruption.

Scientists have a special privilege and responsibility: We are close to the technology itself and therefore well positioned to influence its trajectory. We must work to create an AI-infused world that we want to live in. Technology, as the historian Melvin Kranzberg observed, “is neither good nor bad; nor is it neutral.” Whether the AI we build is detrimental or beneficial to society depends on the choices we make today. But we cannot create a positive future without a vision of what it looks like.

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

Posted on November 5, 2025 at 7:04 AM • View Comments