Design Metrics to Measure Inappropriate Content Severity and Prevalence

Quick Overview

This interview question evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer for Design Metrics to Measure Inappropriate Content Severity and Prevalence states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

Design Metrics to Measure Inappropriate Content Severity and Prevalence

Company: Meta

Role: Data Scientist

Category: Analytics & Experimentation

Difficulty: hard

Interview Round: Technical Screen

##### Scenario Product team is launching a new harmful-content detection system and needs a comprehensive measurement plan. ##### Question Propose metrics to measure the severity and prevalence of inappropriate content. Explain why each is chosen and list pros/cons. Define and justify the View Prevalence metric. Design an online A/B experiment to evaluate the new model: state hypothesis, primary success metric, guardrails, sample-size and runtime estimations, and steps to interpret results. ##### Hints Tie metrics to user harm; weigh severity vs frequency; outline power analysis, segment checks, and risk mitigations.

Quick Answer: This interview question evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer for Design Metrics to Measure Inappropriate Content Severity and Prevalence states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

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Aug 4, 2025, 10:55 AM
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Design Metrics to Measure Inappropriate Content Severity and Prevalence

Harmful-Content Detection: Measurement Plan and Experiment Design

Objective

You are launching a new harmful-content detection system and must define how to measure its impact on user harm and platform health.

Tasks

  1. Propose metrics that measure both the severity and the prevalence of inappropriate content. For each metric, explain why it was chosen and list pros/cons.
  2. Define and justify the View Prevalence metric.
  3. Design an online A/B experiment to evaluate the new model. Include:
    • Hypotheses
    • Primary success metric
    • Guardrail metrics
    • Sample-size and runtime estimations
    • Steps to analyze and interpret results

Hints

  • Tie metrics explicitly to user harm; balance severity and frequency.
  • Include power analysis, segment checks, and risk mitigations.

Constraints & Assumptions

  • Preserve the scope, facts, inputs, and requested outputs from the prompt above.
  • If the prompt leaves a detail unspecified, state a reasonable assumption before relying on it.
  • Keep the answer interview-ready: concise enough to present, but concrete enough to implement or evaluate.

Clarifying Questions to Ask Guidance

  • Clarify the business objective, unit of analysis, time window, exposure definition, and primary metric.
  • State assumptions about instrumentation, randomization, sample size, and data quality.
  • Separate descriptive analysis from causal claims.

What a Strong Answer Covers Guidance

  • A metric framework with primary, guardrail, and diagnostic metrics.
  • A credible analysis or experiment design with clear assumptions and bias checks.
  • SQL/statistical logic for segmentation, variance, confidence, and data validation where relevant.
  • An actionable recommendation that explains trade-offs and next steps.

Follow-up Questions Guidance

  • What sanity checks would you run before trusting the result?
  • How would you handle novelty effects, seasonality, or selection bias?
  • What decision would you make if metrics disagree?
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