Students produced AI policies with meaningful restrictions because the restrictions came from user input. User control worked better when designed with the people affected by it.
what changed when students helped design the policy?
Students produced policies with meaningful restrictions. Their input shaped the rules directly.
That result points to a practical lesson for AI policy work: the people who use a system can contribute restrictions that fit their experience of it.
why did user input lead to better user control?
User control worked better when it was designed with users. The people affected by the policy had a role in defining how it should work.
That makes user input part of the design itself, rather than a reaction after the rules are already set.
where should AI governance and safety settings start?
Governance, safety settings, and acceptable-use design should start with the people who have to live with the rules.
The transferable finding is simple: build the rules with those people. Their participation can produce meaningful restrictions and stronger user control.
How should students contribute to AI policy design?
Students should provide input while the policy is being designed. Their input can produce policies with meaningful restrictions.
Why should users help design AI safety settings?
User control worked better when designed with users. The people affected by the settings can help shape rules they have to live with.
Who should shape acceptable-use rules for AI systems?
The people who have to live with the rules should help build them. That includes users whose input can guide meaningful restrictions.