Friendly Artificial Intelligence (FAI) is a concept in artificial intelligence research that focuses on designing advanced AI systems whose goals and actions are aligned with human values and welfare. The idea is most commonly discussed in the context of powerful, potentially superintelligent agents, where the risk of misalignment between AI objectives and human interests could have significant consequences. FAI is not just about creating an AI that is ‘nice’ or ‘helpful’ in a general sense. Instead, it involves rigorous approaches to ensure that an AI’s behavior consistently benefits humanity, even as it becomes more capable and autonomous.
The term was popularized by philosopher and AI theorist Eliezer Yudkowsky, who argued that as AI systems become more intelligent and able to make decisions independently, it becomes critical to guarantee that they do not inadvertently cause harm. This is particularly important for artificial general intelligence (AGI), which could outperform humans in many domains and be able to act with significant autonomy. Without proper alignment, an advanced AI might pursue its goals in ways that are unsafe or undesirable from a human perspective. Classic thought experiments illustrate the danger, such as a hypothetical AI tasked with making paperclips that converts all available matter into paperclips—including people—if not properly constrained.
Building FAI is a complex technical and philosophical challenge. It requires not only programming explicit rules or ethical guidelines but also developing methods for an AI to understand, learn, and adapt to nuanced human values, which can be ambiguous, conflicting, or difficult to formalize. Researchers working on FAI explore topics like value learning, corrigibility (the willingness of an AI to be corrected or shut down), robustness to distributional shifts, and long-term safety. The field overlaps with ethics of artificial intelligence and existential risk studies, since a failure to ensure friendliness in highly capable AI could pose global risks.
Efforts toward FAI often include research in machine learning, cognitive science, philosophy, and even political science, since value alignment may need to consider fairness, transparency, and broad stakeholder input. Some approaches investigate how to encode human preferences directly, while others focus on indirect methods, such as having the AI learn values by observing human behavior or asking clarifying questions. Ultimately, Friendly Artificial Intelligence is about proactively shaping the development and deployment of advanced AI to ensure it is beneficial, controllable, and safe for everyone.
