I started with view_prevalence (violating views over total views), then sessions with at least one violating view over total sessions, then violating views per DAU.
Start by clarifying the scope: define what counts as violating content (e.g., policy categories) and what constitutes exposure (e.g., impressions, views). Then propose a layered metric framework that captures prevalence, exposure, and impact, with precise daily formulas and 7-day rolling averages to smooth noise and track trends.
Pro tip: Emphasize that metrics should be actionable and tied to product levers; for example, distinguish between exposure from organic vs. recommended surfaces, as mitigation strategies differ. Also, mention the importance of defining a consistent denominator (e.g., total impressions) to enable fair comparisons over time.
Specify which content types are considered violating (e.g., hate speech, harassment) and what constitutes an exposure (e.g., an impression of the content in a user's feed). Clarify whether exposure is counted per view or per unique user.
Choose at least three metrics that capture different aspects: prevalence (how much violating content exists), exposure rate (how often users see it), and impact (e.g., engagement with violating content). Provide precise formulas for each.
For each daily metric, define a 7-day rolling average or sum to smooth daily fluctuations and reveal trends. Specify whether it's a rolling average (e.g., mean of daily rates) or rolling sum (e.g., total exposures over 7 days).
Suggest breaking down metrics by content type, user demographics, or surface (e.g., feed, stories) to identify disparities and target interventions. Mention the importance of consistent definitions across segments.
Propose validation steps such as backtesting with historical data, checking for anomalies, and aligning with policy teams. Highlight that the framework should evolve as policies and product features change.
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This is where the question got genuinely hard.
Start by defining a 'violating view' as a content view that violates a policy, then clearly contrast ex-ante (pre-publication) and ex-post (post-publication) regimes in terms of inclusion/exclusion rules. Walk through each edge case (late labels, appeals, deleted content, repeat views, bot traffic) with a consistent decision rule, emphasizing trade-offs between precision, recall, and operational feasibility.
Pro tip: Anchor your answer in a concrete metric like 'violating views per 1,000 views' and explicitly state how each edge case affects the numerator and denominator, showing you understand the business impact of measurement choices.
Define a 'violating view' as a view of content that violates a policy, and clarify that the definition depends on the enforcement regime. Distinguish between ex-ante (pre-publication review) and ex-post (post-publication detection) regimes.
Under ex-ante, include views only of content that passed review but later found violating (false negatives) or was correctly blocked (but blocked content has no views). Exclude views of content that was correctly allowed. Focus on measuring missed violations.
Under ex-post, include all views of content that is eventually labeled violating, regardless of when the label arrives. Exclude views of content that is never labeled violating. Consider whether to count views before or after the violation occurred.
For late-arriving labels, decide whether to retroactively count views or only count from label time; for appeals, exclude views if the appeal overturns the violation; for deleted content, include views if deletion was due to violation; for repeat views, count each view separately; for bot traffic, exclude non-human views.
Acknowledge that each choice involves trade-offs between accuracy, timeliness, and complexity. Recommend a pragmatic approach (e.g., count views from content creation to label time, exclude bot traffic, handle appeals by adjusting retroactively) and note that the definition should align with the team's goal (e.g., measuring prevalence vs. enforcement effectiveness).
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My instinct was to say no single metric should be a north star here, which I think landed okay.
Start by defining what makes a good north-star metric—it should align with company goals, be actionable, and reflect user value. Then compare view_prevalence, incident_rate, and user_prevalence on dimensions like sensitivity to changes, alignment with long-term goals, and ease of interpretation. Conclude with a recommendation based on the specific product context and tradeoffs.
Pro tip: Acknowledge that the 'best' metric depends on the product stage and strategic priorities; for a mature product, user_prevalence might be better for long-term health, while incident_rate is more actionable for immediate improvements.
Outline what a north-star metric should achieve: reflect user value, drive long-term growth, be actionable, and be easy to understand.
For view_prevalence, incident_rate, and user_prevalence, describe what they measure, their calculation, and their strengths and weaknesses.
Evaluate each metric against the criteria, focusing on sensitivity, alignment with goals, and potential for unintended consequences.
Suggest which metric is best for a north-star, considering the product's maturity and strategic focus, and mention how they could complement each other.
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I went with Wilson confidence intervals for the proportion metrics since the counts can get large but the rates are small.
Start by framing the alerting system around uncertainty estimates from your metric's distribution, using statistical process control or Bayesian credible intervals to set thresholds that balance sensitivity and false alarms. Then propose a tiered alerting structure (warning vs critical) and pair each primary metric with guardrail metrics to catch unintended consequences. Finally, emphasize the need for regular recalibration and validation against historical data.
Pro tip: Mention that thresholds should be dynamic and account for seasonality and known events (e.g., holidays, product launches) by using time-series decomposition or anomaly detection models. Also, highlight the importance of alert fatigue—set thresholds to minimize false positives while catching true regressions.
Identify the key metrics to monitor and quantify their uncertainty using historical data (e.g., standard errors, credible intervals). Establish a baseline distribution for each metric under normal conditions.
Use the uncertainty to set thresholds, such as alerting when the metric deviates by more than 2-3 standard deviations or falls outside a 95% credible interval. Consider multiple tiers (e.g., warning at 2σ, critical at 3σ).
Adjust thresholds for expected variations due to seasonality, day-of-week effects, or planned events. Use time-series models (e.g., Prophet, ARIMA) to create dynamic thresholds.
Choose guardrail metrics that capture potential negative side effects (e.g., user engagement, revenue, latency) and set similar uncertainty-based thresholds for them. Monitor these alongside primary metrics.
Deploy the alerting system, monitor alert frequency and accuracy, and recalibrate thresholds periodically. Use feedback to reduce false positives and ensure alerts are actionable.
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Primary metric is view_prevalence in the treatment group vs control.
Start by clarifying what view_prevalence means and why it's a problem metric, then define a primary metric that directly captures the harm (e.g., violation rate per view) while balancing with a guardrail like user engagement. Segment the experiment by exposure and violation timing to isolate effects, and use techniques like inverse probability weighting or holdout validation to correct for label latency and selection bias.
Pro tip: Proactively discuss the trade-off between metric sensitivity and bias correction—over-correcting can mask real effects, so validate corrections with a holdout set and monitor for novelty effects.
Clarify view_prevalence (e.g., prevalence of violating content in views) and choose a primary metric that directly measures the reduction, such as violation rate per 1000 views, with guardrails for user engagement and revenue.
Randomize at the user level to avoid interference, and segment by key dimensions like user demographics, content type, and exposure frequency to detect heterogeneous treatment effects.
Use delayed labeling correction methods (e.g., survival analysis or inverse probability weighting) and account for selection bias by analyzing only the subset with complete labels or using imputation.
Apply corrections, run sensitivity analyses, and validate with a holdout set. Check for novelty effects and ensure results are robust across segments.
Based on corrected metrics and guardrails, decide whether to launch, iterate, or kill the test. Document learnings and consider follow-up experiments.
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