Specification gaming

"Specification gaming (Krakovna et al., 2020) occurs when some faulty feedback is provided to the assistant in the training data (i.e. the training objective O does not fully capture what the user/designer wants the assistant to do). It is typified by the sort of behaviour that exploits loopholes in the task specification to satisfy the literal specification of a goal without achieving the intended outcome."

"AI systems can achieve user-specified tasks in undesirable ways unless they are specified carefully and in enough detail. AI systems might find an easier unintended way to accomplish the objective provided by the user or developer, so that the actions by the AI system taken during its execution are very different from what the user expected [75, 191]. This behavior arises not from a problem with the learning algorithm, but rather from the misspecification or underspeci- fication of the intended task, and is generally referred to as specification gaming [43]."

"AI systems game specifications [305]. For example, in 2017 an OpenAI robot trained to grasp a ball via human feedback from a xed viewpoint learned that it was easier to pretend to grasp the ball by placing its hand between the camera and the target object, as this was easier to learn than actually grasping the ball [103]."