The Alignment Research Center (ARC) is a nonprofit research organization focused on AI alignment—the problem of developing advanced AI systems whose behavior reliably reflects human goals and interests. Founded in 2021 and based in Berkeley, California, it has become a well-known organization within the AI safety research community for its emphasis on foundational and theoretical research. Alignment Research Center
ARC's stated mission is to align future machine learning systems with human interests. Its work has evolved over time, with recent research emphasizing the development of theoretical foundations for understanding neural networks and producing mechanistic explanations of model behavior. Earlier research explored topics such as scalable oversight and methods for eliciting knowledge that may exist within a model but not be reflected in its outputs.
Rather than primarily building commercial AI systems, ARC concentrates on long-term technical research into alignment. This includes studying how increasingly capable AI systems can be understood, evaluated, and designed to behave reliably even in situations that differ from their training. The organization has also contributed to work on evaluating advanced AI models for potentially dangerous capabilities, helping shape research on frontier model assessments.
ARC was founded by Paul Christiano, a prominent AI alignment researcher formerly at OpenAI. Its publications and research agenda have been influential in the broader AI safety ecosystem, alongside academic groups, nonprofit organizations, and industry research teams working on alignment and model evaluations. While its research priorities represent one perspective within AI safety, they have been widely discussed and have informed subsequent work across the field. Wikipedia
According to ARC's own description, its current emphasis is on developing a theoretical foundation for mechanistic explanations of neural network behavior—work intended to improve scientific understanding of how advanced models internally represent information and make decisions. This reflects a broader goal of making future AI systems more interpretable and easier to align with intended objectives. Alignment Research Center