Entries Tagged "LLM"

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Subverting LLM Coders

Really interesting research: “An LLM-Assisted Easy-to-Trigger Backdoor Attack on Code Completion Models: Injecting Disguised Vulnerabilities against Strong Detection“:

Abstract: Large Language Models (LLMs) have transformed code completion tasks, providing context-based suggestions to boost developer productivity in software engineering. As users often fine-tune these models for specific applications, poisoning and backdoor attacks can covertly alter the model outputs. To address this critical security challenge, we introduce CODEBREAKER, a pioneering LLM-assisted backdoor attack framework on code completion models. Unlike recent attacks that embed malicious payloads in detectable or irrelevant sections of the code (e.g., comments), CODEBREAKER leverages LLMs (e.g., GPT-4) for sophisticated payload transformation (without affecting functionalities), ensuring that both the poisoned data for fine-tuning and generated code can evade strong vulnerability detection. CODEBREAKER stands out with its comprehensive coverage of vulnerabilities, making it the first to provide such an extensive set for evaluation. Our extensive experimental evaluations and user studies underline the strong attack performance of CODEBREAKER across various settings, validating its superiority over existing approaches. By integrating malicious payloads directly into the source code with minimal transformation, CODEBREAKER challenges current security measures, underscoring the critical need for more robust defenses for code completion.

Clever attack, and yet another illustration of why trusted AI is essential.

Posted on November 7, 2024 at 7:07 AM • View Comments

Watermark for LLM-Generated Text

Researchers at Google have developed a watermark for LLM-generated text. The basics are pretty obvious: the LLM chooses between tokens partly based on a cryptographic key, and someone with knowledge of the key can detect those choices. What makes this hard is (1) how much text is required for the watermark to work, and (2) how robust the watermark is to post-generation editing. Google’s version looks pretty good: it’s detectable in text as small as 200 tokens.

Posted on October 25, 2024 at 9:56 AM • View Comments

AI and the SEC Whistleblower Program

Tax farming is the practice of licensing tax collection to private contractors. Used heavily in ancient Rome, it’s largely fallen out of practice because of the obvious conflict of interest between the state and the contractor. Because tax farmers are primarily interested in short-term revenue, they have no problem abusing taxpayers and making things worse for them in the long term. Today, the U.S. Securities and Exchange Commission (SEC) is engaged in a modern-day version of tax farming. And the potential for abuse will grow when the farmers start using artificial intelligence.

In 2009, after Bernie Madoff’s $65 billion Ponzi scheme was exposed, Congress authorized the SEC to award bounties from civil penalties recovered from securities law violators. It worked in a big way. In 2012, when the program started, the agency received more than 3,000 tips. By 2020, it had more than doubled, and it more than doubled again by 2023. The SEC now receives more than 50 tips per day, and the program has paid out a staggering $2 billion in bounty awards. According to the agency’s 2023 financial report, the SEC paid out nearly $600 million to whistleblowers last year.

The appeal of the whistleblower program is that it alerts the SEC to violations it may not otherwise uncover, without any additional staff. And since payouts are a percentage of fines collected, it costs the government little to implement.

Unfortunately, the program has resulted in a new industry of private de facto regulatory enforcers. Legal scholar Alexander Platt has shown how the SEC’s whistleblower program has effectively privatized a huge portion of financial regulatory enforcement. There is a role for publicly sourced information in securities regulatory enforcement, just as there has been in litigation for antitrust and other areas of the law. But the SEC program, and a similar one at the U.S. Commodity Futures Trading Commission, has created a market distortion replete with perverse incentives. Like the tax farmers of history, the interests of the whistleblowers don’t match those of the government.

First, while the blockbuster awards paid out to whistleblowers draw attention to the SEC’s successes, they obscure the fact that its staffing level has slightly declined during a period of tremendous market growth. In one case, the SEC’s largest ever, it paid $279 million to an individual whistleblower. That single award was nearly one-third of the funding of the SEC’s entire enforcement division last year. Congress gets to pat itself on the back for spinning up a program that pays for itself (by law, the SEC awards 10 to 30 percent of their penalty collections over $1 million to qualifying whistleblowers), when it should be talking about whether or not it’s given the agency enough resources to fulfill its mission to “maintain fair, orderly, and efficient markets.”

Second, while the stated purpose of the whistleblower program is to incentivize individuals to come forward with information about potential violations of securities law, this hasn’t actually led to increases in enforcement actions. Instead of legitimate whistleblowers bringing the most credible information to the SEC, the agency now seems to be deluged by tips that are not highly actionable.

But the biggest problem is that uncovering corporate malfeasance is now a legitimate business model, resulting in powerful firms and misaligned incentives. A single law practice led by former SEC assistant director Jordan Thomas captured about 20 percent of all the SEC’s whistleblower awards through 2022, at which point Thomas left to open up a new firm focused exclusively on whistleblowers. We can admire Thomas and his team’s impact on making those guilty of white-collar crimes pay, and also question whether hundreds of millions of dollars of penalties should be funneled through the hands of an SEC insider turned for-profit business mogul.

Whistleblower tips can be used as weapons of corporate warfare. SEC whistleblower complaints are not required to come from inside a company, or even to rely on insider information. They can be filed on the basis of public data, as long as the whistleblower brings original analysis. Companies might dig up dirt on their competitors and submit tips to the SEC. Ransomware groups have used the threat of SEC whistleblower tips as a tactic to pressure the companies they’ve infiltrated into paying ransoms.

The rise of whistleblower firms could lead to them taking particular “assignments” for a fee. Can a company hire one of these firms to investigate its competitors? Can an industry lobbying group under scrutiny (perhaps in cryptocurrencies) pay firms to look at other industries instead and tie up SEC resources? When a firm finds a potential regulatory violation, do they approach the company at fault and offer to cease their research for a “kill fee”? The lack of transparency and accountability of the program means that the whistleblowing firms can get away with practices like these, which would be wholly unacceptable if perpetrated by the SEC itself.

Whistleblowing firms can also use the information they uncover to guide market investments by activist short sellers. Since 2006, the investigative reporting site Sharesleuth claims to have tanked dozens of stocks and instigated at least eight SEC cases against companies in pharma, energy, logistics, and other industries, all after its investors shorted the stocks in question. More recently, a new investigative reporting site called Hunterbrook Media and partner hedge fund Hunterbrook Capital, have churned out 18 investigative reports in their first five months of operation and disclosed short sales and other actions alongside each. In at least one report, Hunterbrook says they filed an SEC whistleblower tip.

Short sellers carry an important disciplining function in markets. But combined with whistleblower awards, the same profit-hungry incentives can emerge. Properly staffed regulatory agencies don’t have the same potential pitfalls.

AI will affect every aspect of this dynamic. AI’s ability to extract information from large document troves will help whistleblowers provide more information to the SEC faster, lowering the bar for reporting potential violations and opening a floodgate of new tips. Right now, there is no cost to the whistleblower to report minor or frivolous claims; there is only cost to the SEC. While AI automation will also help SEC staff process tips more efficiently, it could exponentially increase the number of tips the agency has to deal with, further decreasing the efficiency of the program.

AI could be a triple windfall for those law firms engaged in this business: lowering their costs, increasing their scale, and increasing the SEC’s reliance on a few seasoned, trusted firms. The SEC already, as Platt documented, relies on a few firms to prioritize their investigative agenda. Experienced firms like Thomas’s might wield AI automation to the greatest advantage. SEC staff struggling to keep pace with tips might have less capacity to look beyond the ones seemingly pre-vetted by familiar sources.

But the real effects will be on the conflicts of interest between whistleblowing firms and the SEC. The ability to automate whistleblower reporting will open new competitive strategies that could disrupt business practices and market dynamics.

An AI-assisted data analyst could dig up potential violations faster, for a greater scale of competitor firms, and consider a greater scope of potential violations than any unassisted human could. The AI doesn’t have to be that smart to be effective here. Complaints are not required to be accurate; claims based on insufficient evidence could be filed against competitors, at scale.

Even more cynically, firms might use AI to help cover up their own violations. If a company can deluge the SEC with legitimate, if minor, tips about potential wrongdoing throughout the industry, it might lower the chances that the agency will get around to investigating the company’s own liabilities. Some companies might even use the strategy of submitting minor claims about their own conduct to obscure more significant claims the SEC might otherwise focus on.

Many of these ideas are not so new. There are decades of precedent for using algorithms to detect fraudulent financial activity, with lots of current-day application of the latest large language models and other AI tools. In 2019, legal scholar Dimitrios Kafteranis, research coordinator for the European Whistleblowing Institute, proposed using AI to automate corporate whistleblowing.

And not all the impacts specific to AI are bad. The most optimistic possible outcome is that AI will allow a broader base of potential tipsters to file, providing assistive support that levels the playing field for the little guy.

But more realistically, AI will supercharge the for-profit whistleblowing industry. The risks remain as long as submitting whistleblower complaints to the SEC is a viable business model. Like tax farming, the interests of the institutional whistleblower diverge from the interests of the state, and no amount of tweaking around the edges will make it otherwise.

Ultimately, AI is not the cause of or solution to the problems created by the runaway growth of the SEC whistleblower program. But it should give policymakers pause to consider the incentive structure that such programs create, and to reconsider the balance of public and private ownership of regulatory enforcement.

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

Posted on October 21, 2024 at 7:09 AM • View Comments

Hacking ChatGPT by Planting False Memories into Its Data

This vulnerability hacks a feature that allows ChatGPT to have long-term memory, where it uses information from past conversations to inform future conversations with that same user. A researcher found that he could use that feature to plant “false memories” into that context window that could subvert the model.

A month later, the researcher submitted a new disclosure statement. This time, he included a PoC that caused the ChatGPT app for macOS to send a verbatim copy of all user input and ChatGPT output to a server of his choice. All a target needed to do was instruct the LLM to view a web link that hosted a malicious image. From then on, all input and output to and from ChatGPT was sent to the attacker’s website.

Posted on October 1, 2024 at 7:07 AM • View Comments

AI and the 2024 US Elections

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Posted on September 30, 2024 at 7:00 AM • View Comments

Using LLMs to Exploit Vulnerabilities

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

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

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

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

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

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

Using AI for Political Polling

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

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

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

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

Polling Machines?

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

I know it sounds impossible, but stick with us.

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

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

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

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

Making AI agents better polling subjects

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

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

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

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

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

Likely use cases for AI polling

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

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

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

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

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

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

LLMs Acting Deceptively

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

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

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

AI Will Increase the Quantity—and Quality—of Phishing Scams

A piece I coauthored with Fredrik Heiding and Arun Vishwanath in the Harvard Business Review:

Summary. Gen AI tools are rapidly making these emails more advanced, harder to spot, and significantly more dangerous. Recent research showed that 60% of participants fell victim to artificial intelligence (AI)-automated phishing, which is comparable to the success rates of non-AI-phishing messages created by human experts. Companies need to: 1) understand the asymmetrical capabilities of AI-enhanced phishing, 2) determine the company or division’s phishing threat severity level, and 3) confirm their current phishing awareness routines.

Here’s the full text.

Posted on June 3, 2024 at 7:04 AM • View Comments

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Sidebar photo of Bruce Schneier by Joe MacInnis.