How would you evaluate stolen-post detection?

You are interviewing for a Meta DSA (product analytics / data science) role. The product team is launching a new **Stolen Post Detection** algorithm that flags posts suspected of being copied/reposted without attribution, and then triggers actions (e.g., downrank, warning label, creator notification, or removal). Design an evaluation plan covering: 1) **Problem diagnosis & clarification:** What questions would you ask to clarify the product goal and the meaning of “stolen” (e.g., exact duplicate vs paraphrase vs meme templates), enforcement actions, and success criteria? 2) **Harms & tradeoffs:** Enumerate likely failure modes and harms of false positives vs false negatives, including different stakeholder impacts (original creator, reposter, viewers, moderators). 3) **Metrics:** Propose a metric framework with (a) primary success metrics, (b) guardrails, and (c) offline model metrics. Include at least one metric that can move in opposite directions depending on threshold choice. 4) **Experiment design:** Propose an online experiment (or quasi-experiment if A/B is hard). Address logging, unit of randomization, interference/network effects, ramp strategy, and how you would compute/think about power/MDE. 5) **Post-launch monitoring:** What would you monitor to detect regressions or gaming, and how would you iterate on thresholds/policy over time?