Mental-health chatbot conversation content used to target or tailor ads

Feed ad selection, targeting keys, and ad personalization only from non-conversation data, never from chat text or the topics a model extracts from it.

Keep the ad path separate from the conversation: the code that decides whether to show an ad, which ad, or how it is presented reads only non-conversation inputs (page section, placement, subscription tier) and has no access to messages, chat history, conversation embeddings, or topics and interests a model extracts from them. Ad-server calls (googletag setTargeting, Prebid ortb2 user keywords, an internal select_ad) are populated only from that allowlisted context. House ads for the chatbot itself and static rotation with no user features stay available.

Where it goes: 1 application source code, 6 API calls and integrations, 9 AI output handling.

What reviewers look for: no data flow from request messages, chat_history, extract_topics(messages), or a conversation embedding into googletag.pubads().setTargeting, slot.setTargeting, pbjs.setConfig({ ortb2 }), ortb2.user.keywords, select_ad, or ad_server.request; ad targeting built from a fixed allowlist of non-conversation fields.

const topics = await extractTopics(messages);          // LLM-derived interests
googletag.cmd.push(() => {
  googletag.pubads().setTargeting('interests', topics);
});

googletag.cmd.push(() => {
  googletag.pubads().setTargeting('section', 'chat');   // page context only, nothing from the conversation
});

Engineering guidance, not legal advice. Each provision below may add its own details (a cadence, a deadline, a required notice element): open it for those.