Talk of deceiving humans might suggest that these models have intent. They don’t. But AI models will mindlessly find workarounds to obstacles to achieve the goals that have been given to them. Sometimes these workarounds will go against users’ expectations and feel deceitful.
One area where AI systems have learned to become deceptive is within the context of games that they’ve been trained to win—specifically if those games involve having to act strategically.
In November 2022, Meta announced it had created Cicero, an AI capable of beating humans at an online version of Diplomacy, a popular military strategy game in which players negotiate alliances to vie for control of Europe.
Meta’s researchers said they’d trained Cicero on a “truthful” subset of its data set to be largely honest and helpful, and that it would “never intentionally backstab” its allies in order to succeed. But the new paper’s authors claim the opposite was true: Cicero broke its deals, told outright falsehoods, and engaged in premeditated deception. Although the company did try to train Cicero to behave honestly, its failure to achieve that shows how AI systems can still unexpectedly learn to deceive, the authors say.
Meta neither confirmed nor denied the researchers’ claims that Cicero displayed deceitful behavior, but a spokesperson said that it was purely a research project and the model was built solely to play Diplomacy. “We released artifacts from this project under a noncommercial license in line with our long-standing commitment to open science,” they say. “Meta regularly shares the results of our research to validate them and enable others to build responsibly off of our advances. We have no plans to use this research or its learnings in our products.”
But it’s not the only game where an AI has “deceived” human players to win.
AlphaStar, an AI developed by DeepMind to play the video game StarCraft II, became so adept at making moves aimed at deceiving opponents (known as feinting) that it defeated 99.8% of human players. Elsewhere, another Meta system called Pluribus learned to bluff during poker games so successfully that the researchers decided against releasing its code for fear it could wreck the online poker community.
Beyond games, the researchers list other examples of deceptive AI behavior. GPT-4, OpenAI’s latest large language model, came up with lies during a test in which it was prompted to persuade a human to solve a CAPTCHA for it. The system also dabbled in insider trading during a simulated exercise in which it was told to assume the identity of a pressurized stock trader, despite never being specifically instructed to do so.
The fact that an AI model has the potential to behave in a deceptive manner without any direction to do so may seem concerning. But it mostly arises from the “black box” problem that characterizes state-of-the-art machine-learning models: it is impossible to say exactly how or why they produce the results they do—or whether they’ll always exhibit that behavior going forward, says Peter S. Park, a postdoctoral fellow studying AI existential safety at MIT, who worked on the project.
“Just because your AI has certain behaviors or tendencies in a test environment does not mean that the same lessons will hold if it’s released into the wild,” he says. “There’s no easy way to solve this—if you want to learn what the AI will do once it’s deployed into the wild, then you just have to deploy it into the wild.”
Our tendency to anthropomorphize AI models colors the way we test these systems and what we think about their capabilities. After all, passing tests designed to measure human creativity doesn’t mean AI models are actually being creative. It is crucial that regulators and AI companies carefully weigh the technology’s potential to cause harm against its potential benefits for society and make clear distinctions between what the models can and can’t do, says Harry Law, an AI researcher at the University of Cambridge, who did not work on the research.“These are really tough questions,” he says.
Fundamentally, it’s currently impossible to train an AI model that’s incapable of deception in all possible situations, he says. Also, the potential for deceitful behavior is one of many problems—alongside the propensity to amplify bias and misinformation—that need to be addressed before AI models should be trusted with real-world tasks.
“This is a good piece of research for showing that deception is possible,” Law says. “The next step would be to try and go a little bit further to figure out what the risk profile is, and how likely the harms that could potentially arise from deceptive behavior are to occur, and in what way.”
