In AI governance, what is the goal of incentive-compatible rules?

• A. Align incentives with desired safety outcomes so participants prefer safe behavior.
• B. Punish all attempts at innovation equally.
• C. Hide safety measures from participants to reduce compliance.
• D. Centralize control with no incentives for safe behavior.

Answer: (A)

Incentive-compatible rules are designed to make safe actions the easiest choice for participants. When the rules reward safe behavior and make unsafe behavior less attractive, people and organizations naturally choose safety because it aligns with their own interests. In AI governance this matters because developers, operators, platforms, and users respond to incentives; if safety is rewarded—through outcomes like rewards, reputational benefits, or reduced risk—safe design and deployment become the natural course of action.

So the best choice is the one that says align incentives with the desired safety outcomes so participants prefer safe behavior. Other approaches—punishing all innovation, hiding safety measures, or centralizing control with no safety incentives—tend to backfire: they either stifle progress, reduce transparency, or fail to motivate actors to act safely, making unsafe behavior more likely or harder to deter.