AI Safety | behavior.engineering

AI safety is a research and engineering discipline focused on the risks that arise from advanced AI systems — from near-term problems like harmful outputs and misuse, to longer-term concerns about AI systems that pursue goals humans didn’t intend. In practical product work, AI safety shows up in content moderation, refusal calibration, red-teaming, and the development of behavioral specifications that constrain harmful behavior. For behavior architects, AI safety is the broader field context for the work: much of what they do daily — evaluating for harm, writing policy, designing refusal behavior — is applied AI safety, even if it’s not always labeled that way. Staying connected to the broader safety research community helps practitioners anticipate problems before they become urgent.

Value AlignmentThe degree to which a model's behavior reflects human values, intentions, and goals rather than optimizing for narrow objectives that miss the point.
Harm AvoidanceThe practice of designing model behavior to minimize the risk of producing outputs that cause physical, psychological, social, or financial harm.
Red-TeamingDeliberately attempting to find failure modes, safety vulnerabilities, and policy violations in a model by acting as an adversarial user.
AI EthicsThe study and application of ethical principles — fairness, accountability, transparency, harm avoidance — to the design and deployment of AI systems.
HHH FrameworkA framework developed by Anthropic that identifies Helpful, Harmless, and Honest as the three core properties a well-aligned AI assistant should have.