Design and decompose Trust & Safety risk metrics

You are a Data Scientist in a Trust & Safety team for a short-video platform (similar to TikTok/Reels). The team asks: **“How would you design risk metrics, and how would you decompose () them?”** ## Context The platform faces multiple risk sources and controls: - **Risk types**: policy-violating content (nudity/violence/hate), spam/scams, bot activity, account takeover, misinformation. - **Detection & enforcement signals**: ML model predictions, proactive human review, reactive user reports, and enforcement actions (remove, downrank, age-gate, suspend). ## Task 1) Propose a **metric framework** to measure “risk” at the platform level. - Define **primary metric(s)** and **diagnostic / guardrail metrics**. - Specify exact definitions (numerator/denominator), unit (per view/per user/per content), and time window (e.g., daily in UTC). 2) Explain what it means to **decompose** the risk metric (“”), and provide a concrete **metric tree / breakdown** that helps identify *why* risk increased or decreased. - Show at least two different decomposition approaches (e.g., by funnel stage vs. by segment). 3) List the key **data/measurement pitfalls** and how you would address them. - Examples: label delay, selection bias from user reports, changing enforcement policies, feedback loops from downranking/removal, duplicated content, and seasonality. Your answer should be actionable for ongoing monitoring and root-cause analysis, not just high-level ideas.