Specification design is a critical question in AI because specifications are prone to overspecification, underspecification, and misspecification. We will discuss how experts write reward functions for reinforcement learning (RL) and how non-expert provide preferences for reinforcement learning from human feedback (RLHF). I will show evidence that experts are bad at writing reward functions: even in a trivial setting, experts write specifications that are overfit to a particular RL algorithm, and they often write erroneous specifications for agents that fail to encode their true intent. Next, I will show that the common approach to learning a reward function from non-experts in RLHF uses an inductive bias that fails to encode how humans express preferences, and that our proposed bias better encodes human preferences both theoretically and empirically.