I went with view-weighted prevalence as the primary because it maps more directly to actual user exposure, not just how much bad content exists in the corpus.
Start by clarifying the goal: to measure the severity of harmful content exposure on the platform, not just its presence. Then compare the three prevalence metrics in terms of what they capture and their trade-offs, and recommend a primary metric (likely Reach Prevalence) with severity weighting to reflect user harm. Explain how you would operationalize severity weighting using a rubric or model-based scores, and validate the metric against business and safety objectives.
Pro tip: Acknowledge that no single metric is perfect and propose a composite or tiered approach: use Reach Prevalence for overall severity, but monitor View Prevalence for high-severity content and Content Prevalence for detection gaps. This shows you understand the nuances and can balance competing priorities.
Define what 'harmful content severity' means: the potential harm to users from exposure. Consider Meta's goals to minimize harm while respecting free expression and operational feasibility.
Explain View Prevalence (views of harmful content per total views), Content Prevalence (harmful content per total content), and Reach Prevalence (users who saw harmful content per total users). Discuss their strengths and weaknesses in capturing severity.
Argue for Reach Prevalence as the north-star because it directly measures user exposure and aligns with harm reduction. Justify why it's better than the others for severity.
Propose applying severity weights to each piece of harmful content based on its potential harm (e.g., violence vs. spam). Describe how to derive weights (expert rubric, user surveys, model scores) and aggregate them.
Suggest validation methods: A/B tests, correlation with user reports, and monitoring for unintended consequences. Emphasize the need to iterate as content and user behavior evolve.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the primary harmful content metric and its limitations, then propose supporting metrics that capture prevalence, severity, and user impact. Group metrics into categories like exposure, intensity, and time-weighted, and explain how each complements the primary metric to provide a holistic view.
Pro tip: Emphasize that supporting metrics should be actionable and tied to product decisions, and mention the importance of segmenting by user cohorts to detect disparities and avoid Simpson's paradox.
Define the primary harmful content metric (e.g., prevalence rate) and discuss its limitations, such as not capturing severity or repeated exposure.
Break down harm into dimensions: reach (how many users see it), intensity (severity per exposure), and duration (time spent). This ensures comprehensive coverage.
Suggest specific metrics like user reach (unique users exposed), tail exposure intensity (e.g., 95th percentile of severity), and time-weighted exposure (average time spent on harmful content per user).
Describe how each metric adds nuance: reach shows scale, intensity highlights worst cases, and time-weighted exposure captures cumulative impact.
Acknowledge challenges like data collection, metric correlation, and potential trade-offs between metrics, and suggest how to prioritize them based on product goals.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by defining View Prevalence and its role as a harmful content metric, then systematically evaluate its strengths and limitations. Structure your answer by first discussing what it captures well, then what it misses, and finally suggest complementary metrics or adjustments to provide a more holistic view.
Pro tip: Acknowledge that no single metric is perfect and emphasize the importance of aligning metrics with business goals and user safety. Show awareness of trade-offs between simplicity and comprehensiveness, and mention how View Prevalence can be gamed or misinterpreted.
Clearly explain what View Prevalence measures: the proportion of content views that are harmful, often calculated as (views of harmful content) / (total views).
Discuss what View Prevalence captures well, such as its simplicity, interpretability, and ability to reflect user exposure to harmful content at scale.
Highlight its limitations: it may overlook severity, context, user impact, and can be skewed by outliers or viral content; it also doesn't account for prevention or detection efforts.
Propose additional metrics like prevalence per user, severity-weighted prevalence, or engagement metrics to provide a more nuanced view.
Summarize that View Prevalence is useful but should be part of a broader metric suite, and suggest how to balance it with other measures to drive informed decisions.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Blanked briefly on calibration and just started talking about precision/recall before getting back on track.
Frame your answer around a measurement system that balances statistical rigor with operational feasibility, emphasizing how you'd validate each component (sampling, labeling, classifiers) to minimize bias and latency. Highlight trade-offs between timeliness and accuracy, and propose a continuous monitoring loop with segmentation to catch drift and disparities.
Pro tip: Propose a hybrid approach: use classifiers for rapid, broad measurement but calibrate them against a gold-standard human-labeled sample, and implement a stratified sampling design that oversamples high-risk segments to ensure timely detection of emerging harms.
Clarify what harmful content types you're measuring, the target population, and potential sources of bias (e.g., sampling, labeling, model). Establish metrics like prevalence and recall with confidence intervals.
Use stratified random sampling across key dimensions (e.g., content type, user demographics, time) to ensure coverage. Consider oversampling rare or high-risk segments and implement streaming sampling for real-time monitoring.
Develop clear guidelines, train annotators, and measure inter-annotator agreement. Use techniques like consensus labeling or expert adjudication to reduce label noise and bias.
Train classifiers on labeled data, then calibrate probabilities (e.g., Platt scaling) and evaluate performance across segments. Use confidence intervals and bootstrapping to quantify uncertainty in prevalence estimates.
Continuously track metrics across segments, detect drift, and re-calibrate as needed. Set up alerts for significant deviations and periodically audit the entire pipeline for bias.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.