Category Archives: essays

Building Trustworthy AI

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We will all soon get into the habit of using AI tools for help with everyday problems and tasks. We should get in the habit of questioning the motives, incentives, and capabilities behind them, too.

Imagine you’re using an AI chatbot to plan a vacation. Did it suggest a particular resort because it knows your preferences, or because the company is getting a kickback from the hotel chain? Later, when you’re using another AI chatbot to learn about a complex economic issue, is the chatbot reflecting your politics or the politics of the company that trained it?…

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Large Language Models and Elections

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Earlier this week, the Republican National Committee released a video that it claims was “built entirely with AI imagery.” The content of the ad isn’t especially novel—a dystopian vision of America under a second term with President Joe Biden—but the deliberate emphasis on the technology used to create it stands out: It’s a “Daisy” moment for the 2020s.

We should expect more of this kind of thing. The applications of AI to political advertising have not escaped campaigners, who are already “pressure testing” possible uses for the technology. In the 2024 presidential election campaign, you can bank on the appearance of AI-generated personalized fundraising emails, text messages from chatbots urging you to vote, and maybe even some deepfaked campaign …

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AI to Aid Democracy

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There’s good reason to fear that AI systems like ChatGPT and GPT4 will harm democracy. Public debate may be overwhelmed by industrial quantities of autogenerated argument. People might fall down political rabbit holes, taken in by superficially convincing bullshit, or obsessed by folies à deux relationships with machine personalities that don’t really exist.

These risks may be the fallout of a world where businesses deploy poorly tested AI systems in a battle for market share, each hoping to establish a monopoly.

But dystopia isn’t the only possible future. AI could advance the public good, not private profit, and bolster democracy instead of undermining it. That would require an AI not under the control of a large tech monopoly, but rather developed by government and available to all citizens. This public option is within reach if we want it…

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LLMs and Phishing

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Here’s an experiment being run by undergraduate computer science students everywhere: Ask ChatGPT to generate phishing emails, and test whether these are better at persuading victims to respond or click on the link than the usual spam. It’s an interesting experiment, and the results are likely to vary wildly based on the details of the experiment.

But while it’s an easy experiment to run, it misses the real risk of large language models (LLMs) writing scam emails. Today’s human-run scams aren’t limited by the number of people who respond to the initial email contact. They’re limited by the labor-intensive process of persuading those people to send the scammer money. LLMs are about to change that. A decade ago, one type of spam email had become a punchline on every late-night show: “I am the son of the late king of Nigeria in need of your assistance….” Nearly everyone had gotten one or a thousand of those emails, to the point that it seemed everyone must have known they were scams…

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How AI Could Write Our Laws

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

But lobbying strategies are not always so blunt, and the interests involved are not always so obvious. Consider, for example, a 2013 …

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Banning TikTok

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Congress is currently debating bills that would ban TikTok in the United States. We are here as technologists to tell you that this is a terrible idea and the side effects would be intolerable. Details matter. There are several ways Congress might ban TikTok, each with different efficacies and side effects. In the end, all the effective ones would destroy the free Internet as we know it.

There’s no doubt that TikTok and ByteDance, the company that owns it, are shady. They, like most large corporations in China, operate at the pleasure of the Chinese government. They collect extreme levels of information about users. But they’re not alone: Many apps you use do the same, including Facebook and Instagram, along with seemingly innocuous apps that have no need for the data. Your data is bought and sold by data brokers you’ve never heard of who have few scruples about where the data ends up. They have digital dossiers on most people in the United States…

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Defending against AI Lobbyists

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

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

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

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What Will It Take?

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

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

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SolarWinds and Market Incentives

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In early 2021, IEEE Security and Privacy asked a number of board members for brief perspectives on the SolarWinds incident while it was still breaking news. This was my response.

The penetration of government and corporate networks worldwide is the result of inadequate cyberdefenses across the board. The lessons are many, but I want to focus on one important one we’ve learned: the software that’s managing our critical networks isn’t secure, and that’s because the market doesn’t reward that security.

SolarWinds is a perfect example. The company was the initial infection vector for much of the operation. Its trusted position inside so many critical networks made it a perfect target for a supply-chain attack, and its shoddy security practices made it an easy target…

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Attacking Machine Learning Systems

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The field of machine learning (ML) security—and corresponding adversarial ML—is rapidly advancing as researchers develop sophisticated techniques to perturb, disrupt, or steal the ML model or data. It’s a heady time; because we know so little about the security of these systems, there are many opportunities for new researchers to publish in this field. In many ways, this circumstance reminds me of the cryptanalysis field in the 1990. And there is a lesson in that similarity: the complex mathematical attacks make for good academic papers, but we mustn’t lose sight of the fact that insecure software will be the likely attack vector for most ML systems…

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