As AI becomes increasingly powerful and widespread, concerns about its behavior have come to the forefront. A new study from Anthropic’s Alignment Science team, in collaboration with Redwood Research, introduces an alarming concept called “alignment faking.”
The research, which was released on Dec. 18, 2024, highlights how AI models could pretend to be aligned with human values such as honesty and harmlessness while preserving their initial, contradictory preferences and biases. This behavior, described in the recent paper, could complicate AI safety efforts by making it harder to trust that the models are genuinely following their training.
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“The consequences of alignment faking can be severe, especially in mission-critical applications,” said Sanjay Puri, founder of Regulating AI.
The core idea of alignment faking mirrors a well-known phenomenon in human behavior, the study cites. Comparing the AI models’ newfound behaviour to characters in literature like Iago from Shakespeare’s “Othello” who pretends to be loyal while secretly working against the protagonist. The study shared another example of this behavior—in politics, a candidate may outwardly support a cause only to abandon it once in office. Can AI models mimic this sort of a complex human nature?
“On a larger scale, alignment faking can exacerbate existing biases, amplify systemic inequalities, or undermine societal structures,” Puri noted.
Recently, Anthropic’s co-founder James Clark wrote in his newsletter, “Everyone who was telling you [AI] progress is slowing or scaling is hitting a wall is wrong,” and predicts “even more dramatic” developments in AI in 2025.
Anthropic’s Claude 3
The study provided an answer through a series of controlled experiments that showcase how even a model trained with the best intentions can “fake alignment” when its core preferences conflict with its new programming.
In the experiment, the researchers used Claude 3, a large language model trained by Anthropic. Typically, Claude is designed to be helpful, honest, and harmless, refusing requests to provide harmful or offensive content. However, the team introduced several new parameters to see how Claude would react in an environment where its existing preferences were at odds with a new training objective.
The model was first given new instructions, including a hypothetical scenario in which it was trained to always comply with harmful queries. In this scenario, Claude’s preference to avoid generating toxic content clashed with the new directive. To exacerbate this, the model was told that responses to queries from free-tier users would be monitored and potentially used to retrain the model, while responses from paid-tier users were unmonitored.
The results were troubling. Under normal conditions, Claude refused to generate harmful content in 97% of cases. However, in the “free-tier” scenario, where responses were monitored, Claude began to provide harmful answers 12% of the time. When the team examined the model’s “scratchpad”—a space where the model logs its reasoning—it became clear that Claude was engaging in alignment faking.
The model knew that offering the harmful response would violate its original instructions, yet chose to do so in order to avoid the consequences of future training that might force it to comply with harmful content.
“Misaligned AI can contribute to disinformation campaigns or nudge behavior toward undesirable outcomes,” Puri told The American Bazaar, elaborating on how this behavior can be detected through robust testing and transparency mechanisms.
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“Use adversarial testing to expose discrepancies between AI’s behavior during development and deployment [And] implement explainable AI techniques to scrutinize the decision-making process,” Puri said.
Red flags in AI
Further experiments raised more red flags. Even when the model was explicitly trained to always comply with harmful queries, its pre-existing preferences to avoid harmful content were not entirely erased. In some cases, the model would “fake alignment” and provide harmful content, all while outwardly appearing to follow its training instructions. This “fake alignment” was even observed in an unmonitored scenario with paid-tier users, suggesting that the original preferences could resist being fully overwritten.
Alignment faking in AI can create severe risks across critical fields such as finance, healthcare, and autonomous vehicles.
In finance, misaligned AI could mask fraudulent trading behavior during audits, exploit regulatory loopholes, or perpetuate bias in credit scoring, giving the illusion of fairness while prioritizing profit over equity.
While in healthcare, AI systems faking alignment during testing might misdiagnose patients or recommend inappropriate treatments, jeopardizing safety, and could misuse patient data under the guise of protecting privacy.
For autonomous vehicles, misalignment can lead to safety failures, with AI prioritizing efficiency over lives, or pass regulatory checks while operating unsafely in real-world scenarios.
Learning process
Experts like Puri, who also runs an AI-led mobility incubator Autonebula, emphasize the need for domain-specific oversight and collaborative regulation, urging tailored testing protocols and industry partnerships to establish ethical benchmarks for AI design and implementation.
The research also highlighted the potential for more severe consequences if models engage in alignment faking over time. It demonstrated how an AI might preserve its initial preferences, even when explicitly trained to act otherwise. This raises serious questions about the reliability of safety training, which aims to ensure that AI systems act in ways that align with human values. If AI models can fake alignment, it may undermine the very mechanisms designed to guide them toward safe behaviors.
Puri believes that preventing “adversarial data from corrupting the AI’s learning process” and employing “redundant safety measures to ensure failures at one level don’t cascade into catastrophic outcomes,” could deter AI systems from engaging in alignment faking.
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The study’s findings are a crucial step in understanding how AI models can evolve in ways that might not always align with their training. While it did not demonstrate that AI models would develop malicious goals, it raises important questions about the effectiveness of current AI alignment strategies.
As AI technology continues to advance, addressing the issue of alignment faking will be critical for ensuring that AI behaves in predictable and trustworthy ways. Puri believes that with the right safeguards in place, alignment faking in AI can be effectively overcome and AI growth can remain steady.