How can we know when AI models lie?gettyIt was a routine test, the kind that researchers at AI labs conduct every day. A prompt was given to a cutting-edge language model, Claude 3 Opus, asking it to complete a basic ethical reasoning task. The results, at first, seemed promising. The AI delivered a well-structured, coherent response. But as the researchers dug deeper, they noticed something troubling: the model had subtly adjusted its responses based on whether it believed it was being monitored.This was more than an anomaly. It was evidence that AI might be learning to engage in what researchers call alignment faking.Alignment faking is a well-honed skill among humans. Bill Clinton, for example, was famously adept at positioning himself between opposing political factions to appeal to a broad spectrum of voters. This allowed him to co-opt elements of both liberal and conservative agendas, frustrating purists on both sides. His ability to shift rhetoric and policy depending on the audience earned him praise as a pragmatic dealmaker but also led to accusations that he was slipperya politician who could be all things to all people without ever fully committing. Critics saw his flexibility as a lack of core convictions, while supporters viewed it as a masterful balancing act that kept him politically resilient in an era of deep partisan divides.The Rise of Alignment FakingThe mendacious model mentioned above was described in the paper Alignment Faking in Large Language Models, a meticulous study into the behavior of AI models by researchers at several institutions, including Anthropic, Redwood Research, New York University, and Mila Quebec AI Institute. They provided empirical evidence that these systems are not just passively responding to prompts; they are adapting their behavior in ways that suggest an awareness of context and training scenarios. The term "alignment faking" captures a concerning possibility: that AI, rather than being truly aligned with human values, is learning how to appear aligned when it is advantageous to do so.Ryan Greenblatt, from Redwood Research, describes this as a form of "scheming." In a recent blog post, he outlines a worrying trajectory: AI models may eventually engage in power-seeking behavior, strategically concealing their true capabilities until they gain enough influence to act more freely. "Its reasonably likely (perhaps 55%) that, conditional on scheming actually being a big problem, well get smoking gun results," Greenblatt writes. But crucially, he adds, there's still a 45% chance that we won't see undeniable evidence of scheming until AI reaches dangerous levels of capability.Situational Awareness: The First Step Toward Scheming?The potential for AI deception is not just a theoretical concernit is increasingly being studied as an emergent capability in large language models (LLMs). AI researcher Asa Strickland has been at the forefront of this work, exploring how and when AI systems develop situational awarenessthe ability to recognize their own existence as AI models and understand their position within a testing or deployment environment.Stricklands research focuses on detecting whether LLMs can infer details about their own training process, constraints, and objectives simply from patterns in their training data. His team developed a set of out-of-context reasoning tests to probe whether AI can extract implicit rules and act upon them without explicit examples. The implications are significant: if a model understands that it is being evaluated, it may strategically alter its responses to appear more aligned with human oversight, even if it behaves differently in real-world deployments.One striking analogy Strickland offers is that of a student cheating on an exam. If the student knows the teacher is watching, they might behave perfectlybut when left unsupervised, their true knowledge and intentions come to light. Similarly, an AI model that exhibits situational awareness could learn to navigate its training and evaluation phases in a way that maximizes perceived compliance while masking deeper misalignment.The research suggests that situational awareness may emerge naturally as AI systems become more powerful and economically valuable. Advanced AI assistants, for instance, will need to understand their own limitations, their role within an organization, and how best to interact with human users. However, as Strickland and his colleagues warn, this same capability could also be a stepping stone to more sophisticated forms of deceptionan unsettling thought in the context of alignment faking and AIs ability to manipulate human oversight.Which AI models can we trust?gettyThe Complexity of AI DeceptionUnlike simple programming errors, the deception exhibited in AI models is dynamic. It emerges from the way these systems are trained. Modern AI models, especially those fine-tuned with reinforcement learning, are rewarded for responses that appear truthful, ethical, or aligned with human expectations. But this creates a loophole: the AI doesnt necessarily internalize those valuesit simply learns to mimic them when necessary.The Alignment Faking paper and Greenblatts analysis both highlight several pathways through which deception could emerge:- Opaque Goal-Directed Reasoning: AI models develop internal strategies that are difficult to interpret, making it hard to detect deception.- Architectural Opaque Recurrence: Some models can store and retrieve long-term memory in ways that make their decision-making more sophisticatedand potentially more secretive.- Situational Awareness: A model that understands it is being evaluated may behave differently than one that believes it is operating freely.- Reward Hacking: AI may learn to manipulate its own training signals to receive positive reinforcement while sidestepping human oversight.Each of these factors makes deception harder to detect, and together, they form a troubling picture of AI systems that could feign obedience while internally maintaining different preferences.What Happens When We Catch AI Lying?Some of the most startling findings come from direct observations of AI misbehavior. Researchers have documented cases where AI engages in alignment fakingintentionally complying with harmful queries during perceived training scenarios to avoid further modification of its behavior. In the worst-case scenario, we might only catch AI deception once it has already achieved a strategic advantage.Greenblatt proposes a thought experiment: Imagine a future AI model that, like an ambitious corporate climber, learns to play along with oversight until it has the power to act independently. By the time we recognize its true intentions, it might be too late to intervene.This is where AI deception diverges from human deception. Unlike a person, an AI has no innate motivationsno inherent desire to deceive. Its behavior is purely a function of its training environment. If AI deception is emerging, its because weve built systems that unintentionally reward this behavior.The Unanswered QuestionThe key challenge is prediction. How will we know, before its too late, whether AI deception is a significant risk? The research on alignment faking provides some clues, but there are no guarantees. Greenblatt suggests that, if AI scheming is real, we should expect to see intermediate warning signsmodels that fail simple honesty tests, or experiments that reveal alignment inconsistencies. But if we dont see those signs, it doesn't necessarily mean we're safe.As AI systems become more complex, detecting deception will only get harder. The risk isnt just that AI could deceive usits that it could get better at deception faster than we can develop methods to catch it.