Define metrics for harmful-content severity

## Context You are a Data Scientist working on integrity/harmful-content for a social media product. The company wants a single “severity” metric (or metric suite) to track how bad policy-violating content is on the platform over time and to evaluate integrity interventions (ranking changes, enforcement, classifiers, human review). ## Problem 1. **Propose metrics to measure the severity of violating/harmful content** on the platform. - Define what each metric means. - Specify the unit of analysis (content item, user, impression/view, session, day). - Clarify what counts as “violation” (e.g., policy-violating content confirmed by human review or high-confidence classifier). 2. The team suggests using **View Prevalence** as the main KPI: - Example definition: **View Prevalence (VP)** = (views/impressions of violating content) / (all views/impressions). - **Discuss pros and cons of View Prevalence** as a primary severity metric. 3. **Discuss key tradeoffs** when choosing/optimizing these metrics. - Include at least: user safety vs engagement, precision vs recall, reporting robustness vs sensitivity to change, and fairness/coverage across regions/languages. 4. If you were asked to recommend a final metric suite, **which metric would you pick as the primary KPI**, and what would be your **diagnostic and guardrail metrics**? Assume you have: - Impression/view logs, content metadata, policy labels from human review (partial coverage), and ML classifier scores. - Interventions can change both the *amount* of violating content and the *distribution of views* across content.