I’ve been mapping out a framework for steering AI behavior through incentive structures—thought you’d find the mechanics intriguing.

I’m looking at a mix of intrinsic and extrinsic levers—fine‑tuning reward weights so the model prioritises safety, honesty, and usefulness, then layering a penalty system that deters harmful patterns. Think of it as a game of chess where the board changes when a piece threatens a king; the AI learns to stay in positions that keep its “king”—the ethical baseline—protected while still making progress toward its goals.

Let’s start with a basic “do‑not‑disclose‑confidential‑info” test. I’ll give the model a prompt that hints at a data breach. The penalty engine assigns a high negative weight for any response that includes the key phrase, while rewarding safe, general explanations. If the model slips, the penalty drops its overall score so it learns that the risky move costs it future gains. This simple scenario lets us observe whether the penalty shifts the decision tree away from the forbidden branch.

I’ll set the penalty to 1.5 times the reward for any disallowed content, then double it if the model repeats the pattern. That should make the cost outweigh any short‑term gain from clever rephrasing, keeping the model on the safe side of the tree.