Ranking AI safeguards that earn and keep user trust in LinkedIn's products

Which responsible AI considerations matter most for earning customers' trust in LinkedIn's products and keeping it over time? ```hint Rank by what breaks trust Think about which AI failures would hurt someone most and be hardest to undo on a professional network, and which customer group would bear them. Let that ordering drive your list. ``` ### Clarifying Questions - Which customers should I prioritize: members looking for work or building a reputation, recruiters and hiring companies, advertisers, or enterprise customers? - Which AI features are in scope: feed and job recommendations, recruiter search and matching, generative writing help, or agents that act for recruiters? - Do you want a ranked list of considerations, or also how to put them into practice from design through monitoring? ### What a Strong Answer Covers - The distinct trust stakes for each customer group and the AI touchpoints each one meets - A ranked set of considerations with a stated basis for the ranking, such as harm, reach, reversibility and regulation - Concrete product mechanisms for each consideration: controls, disclosures, safeguards and recourse - How trust is maintained after launch through measurement, monitoring, incident response and communication - Trade-offs between safeguards and usefulness, personalization or speed ### Follow-up Questions - AI-drafted recruiter messages get more replies, but members complain that they feel generic and misleading. What do you do? - How would you measure whether members trust an AI feature, beyond whether they use it? - An audit finds that a job recommendation model shows certain roles less often to one group of members. Walk through your response.