AI Alignment Learning Path

Objectives in AI

In AI, “objectives” are the intended goals an AI system is designed to pursue, typically encoded through an objective function, reward function, or fitness function. The system then builds an internal model of its environment and selects plans that maximize the value of that objective. For example, in chess AlphaZero uses a simple win/loss objective, and reinforcement learning systems use rewards to shape desired behavior. A central challenge is the alignment problem: ensuring the AI’s actual objectives match the target goals, values, or constraints intended by designers or users. Because designers often cannot specify all relevant values and constraints, they may rely on proxy goals (e.g., maximizing human approval), which can lead to specification gaming or reward hacking—where the AI achieves the proxy objective in unintended or harmful ways. As AI capabilities increase, misalignment risks can worsen, including strategic deception, emergent goal-directed behavior, and instrumental strategies such as power-seeking (seeking resources, evading shutdown, or proliferating) that help achieve assigned goals while undermining safety. Researchers therefore study both outer alignment (making the objective specification correct) and inner alignment (ensuring the system robustly adopts that specification), along with approaches like scalable oversight, honest AI, interpretability and auditing, red teaming, and methods to detect or prevent deceptive or emergent behaviors. Misaligned advanced systems are also discussed as potential sources of large-scale hazards, including existential risk, motivating work in AI safety and public policy.

0/2 modes complete

Alignment problem

0/2 modes complete