This is where I spent too long philosophizing.
Start by clarifying that severity is a multi-dimensional construct that should be defined based on the decision it supports, then propose a hybrid representation that combines ordinal levels for interpretability with a continuous score for prioritization. Walk through the signals you'd use, how you'd validate the scale, and how it would drive actions like enforcement or ranking.
Pro tip: Emphasize that the best representation depends on the use case—binary for clear-cut policy violations, ordinal for triage, continuous for ranking—and that you'd validate any scale against human judgments and downstream metrics. Show awareness of trade-offs like false positives vs. false negatives and the cost of misclassification.
Identify who will use the severity score and what decisions it informs (e.g., removal, demotion, warning). This determines the required granularity and actionability.
Break down severity into key dimensions such as harm type (physical, emotional), intent, reach, and vulnerability of the audience. This ensures a comprehensive and defensible definition.
Choose observable signals (e.g., user reports, text classifiers, engagement patterns) and decide how to combine them into a score. Consider human labeling for ground truth.
Decide between binary, ordinal, or continuous based on trade-offs, then validate with human judgments and test impact on decisions. Iterate as needed.
Integrate the severity score into workflows, set thresholds for actions, and monitor for drift, bias, and unintended consequences.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I started with severity-weighted exposure as my primary metric and the interviewer seemed to like that framing.
Start by clarifying the intervention's goal (e.g., reduce exposure to harmful content) and the trade-offs (e.g., user engagement). Then define a primary metric that directly measures the intervention's success, supported by diagnostic metrics to explain changes and guardrail metrics to ensure no harm to user experience or platform health. Ensure each metric has a clear numerator, denominator, and weighting where applicable.
Pro tip: Emphasize that the primary metric should be a direct measure of harmful content exposure (e.g., prevalence per user), not just a proxy like reports, and that guardrails must include both user experience (e.g., DAU) and ecosystem health (e.g., creator impact).
Understand what the intervention aims to achieve (e.g., reduce harmful content) and potential side effects (e.g., reduced engagement). This sets the context for metric selection.
Choose a metric that directly measures the intervention's success. For harmful content, a good primary metric is prevalence of harmful content exposure per user, defined as (number of harmful content views) / (total content views) weighted by view duration or user count.
Select metrics that help explain changes in the primary metric, such as report rate, removal rate, and user engagement with harmful content. These provide insight into why the primary metric moved.
Identify metrics that must not degrade, such as daily active users, overall engagement, and creator diversity. These ensure the intervention doesn't cause unintended harm.
For each metric, clearly state the numerator, denominator, and any weighting (e.g., by time, user, or content type) to ensure precise measurement and avoid ambiguity.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Spillover is the real beast here and I knew it going in.
Start by clarifying the goal: to measure the causal effect of a harmful content intervention on user behavior. Then discuss randomization units (user, post, cluster) and their trade-offs, emphasizing how interference and spillover can bias results. Propose a design that balances validity and practicality, such as cluster randomization with graph-aware clustering, and suggest methods to detect and mitigate interference.
Pro tip: Acknowledge that perfect isolation is impossible on social networks, so focus on quantifying and bounding interference rather than eliminating it. Mention that you would run a pilot to estimate spillover and adjust the design accordingly.
Define the intervention, the outcome metric, and the unit of analysis. Consider practical constraints like implementation complexity and user experience.
Compare user-level, post-level, and cluster randomization. Discuss trade-offs: user-level minimizes spillover but may be infeasible if intervention is at post level; post-level can have interference via shared users; cluster randomization can contain spillover but reduces power.
Propose methods to handle interference: graph-aware clustering (e.g., using community detection), saturation designs, or ego-network randomization. Also consider measuring spillover via network exposure models.
Select a randomization unit and design that best balances internal validity, statistical power, and operational feasibility. For example, cluster randomization by tightly-knit communities with a saturation design.
Outline analysis methods that account for interference, such as cluster-robust standard errors, causal inference under interference, and sensitivity checks. Suggest A/A tests and pilot studies to validate assumptions.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The rare-but-severe vs common-low-severity framing is genuinely hard and I think I gave an okay answer about weighting by expected harm rather than frequency.
Structure your answer by first acknowledging that measuring harmful content at scale is inherently noisy and biased, then systematically walk through the major biases (reporting bias, labeling drift, severity imbalance) and their implications. Finally, propose a decision framework that weights rare severe harms appropriately and balances them against common low-severity issues to make a defensible launch recommendation.
Pro tip: Emphasize that rare severe harms often have outsized user trust and regulatory impact, so use a risk-adjusted metric (e.g., expected harm) rather than raw prevalence. Also, mention that you would set up a continuous monitoring system to detect labeling drift and reporting bias shifts post-launch.
Discuss reporting bias (users report only a fraction of harmful content, and reporting rates vary by severity, user demographics, and platform features) and labeling drift (annotator guidelines and interpretations change over time, leading to inconsistent labels).
Explain that common low-severity harms (e.g., mild insults) dominate volume, while rare severe harms (e.g., credible threats, CSAM) are critical but sparse. Use stratified sampling and oversample severe cases to get reliable estimates.
Propose metrics that account for both prevalence and severity, such as expected harm (prevalence × severity weight) or precision/recall at high-severity thresholds. Avoid relying solely on overall prevalence or accuracy.
Quantify uncertainty from biases and sampling error (e.g., Bayesian models, confidence intervals). Perform sensitivity analyses to see how conclusions change under different assumptions about reporting rates and severity weights.
Synthesize findings into a clear recommendation: if severe harms are within acceptable risk thresholds and mitigation plans are in place, recommend launch with guardrails; otherwise, recommend delaying or limiting launch. Include a monitoring plan to track biases and harms post-launch.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.