A product team wants to use a Claude-based system to provide feedback on user-submitted creative writing. Users include minors (ages 13+). The team asks the architect to ensure the system will not provide feedback that enables harmful content creation. In the context of Constitutional AI principles, what system design approach is most appropriate?
Select an answer to reveal the explanation.
Short Explanation and Infographic
Here's the deal — b is correct because Constitutional AI principles emphasize encoding explicit values and behavioral guidelines appropriate to context: a system serving minors needs usage policies specifically designed for that audience, system prompt instructions that operationalize those policies for Claude, and technical controls (input/output filtering) that provide defense-in-depth. The system should remain useful (craft feedback on legitimate content) while enforcing age-appropriate boundaries.
Full explanation below image
Full Explanation
B is correct because Constitutional AI principles emphasize encoding explicit values and behavioral guidelines appropriate to context: a system serving minors needs usage policies specifically designed for that audience, system prompt instructions that operationalize those policies for Claude, and technical controls (input/output filtering) that provide defense-in-depth. The system should remain useful (craft feedback on legitimate content) while enforcing age-appropriate boundaries. A is wrong because blocking all content mentioning violence or conflict would make the system useless for legitimate creative writing (most literature involves conflict); the policy must target harmful content, not difficult themes. C is wrong because Claude's default safety training is calibrated for general audiences — deploying to minors requires additional context-specific configuration and stricter content policies than the defaults provide. D is wrong because human review of every submission before feedback creates latency incompatible with a writing feedback tool and is economically prohibitive at scale; automated controls should handle the majority of cases, with human review reserved for flagged edge cases.