Detect and Respond to Proxy Metric Divergence

A video platform's recommender is trained to maximize predicted watch-time. Over four successive monthly model releases, the team observes: watch-time per session rising each release (+3%, +4%, +5%, +6%), while a monthly satisfaction survey score has fallen (-1%, -2%, -4%, -7% relative to baseline) and 60-day retention (measured on the cohort exposed to each release, once matured) has also fallen after release 3 and release 4.

1. What does this pattern indicate about the relationship between the proxy and the true objective, and at what point should the team have intervened?
2. Design a concrete guardrail-metric monitoring scheme that would have caught this earlier than a full 60-day retention read.
3. Propose a change to the training objective itself (not just monitoring) that would address the root cause, and explain specifically what problem it solves that better monitoring alone would not.