Investigate Harassment Surge and Mitigation
Problem: Investigating a Harassment Surge
Suppose your monthly violation analysis, using consistent moderation settings, indicates a sharp increase in "Harassment" violations during the most recent month.
Discuss the following aspects of this situation:
1. Plausible Explanations for the Surge
Identify the most plausible explanations for this surge. Consider both real-world behavioral changes and measurement-related artifacts. Examples include:
- A genuine increase in abusive behavior.
- Changes in traffic mix across different surfaces, regions, languages, or creator cohorts.
- Seasonality or external events influencing user behavior.
- Coordinated attacks or the activity of repeat offenders.
- Changes in policy definitions.
- Adjustments to model thresholds or the deployment of new model versions.
- Calibration drift in the underlying classification model.
- Issues related to data quality, logging, or data backfills.
2. Investigation Strategy
Detail how you would investigate whether the surge is a real phenomenon or an artifact of the measurement system. Be specific about:
- The prevalence metrics you would employ.
- The appropriate denominators and time windows for comparison.
- The segments you would use to break down the data for deeper analysis.
- Additional datasets you would request, such as human-review labels, user reports, enforcement logs, or model-version metadata.
- Methods to account for confounding variables, selection bias, and Simpson's paradox.
3. Proposed Solutions (If Surge is Real)
If the investigation confirms the surge is real, propose a range of solutions. These should encompass product, policy, ranking, operational, and machine learning interventions. Include both short-term containment actions and longer-term strategic fixes.
4. Evaluation of Solutions
Describe how you would evaluate the effectiveness of your proposed solutions. This should cover:
- Primary and guardrail metrics.
- Experiment or rollout design methodologies.
- Key tradeoffs, specifically considering false positives, fairness, and overall user experience.
Throughout your discussion, ensure you distinguish between a volume increase and a rate increase in violations. Additionally, explain how you would validate model-driven metrics using human-reviewed labels.
AI interview prep
Core dilemma
Distinguishing genuine behavioral shifts from measurement artifacts in a high-stakes, sensitive domain, while balancing speed of response with analytical rigor and considering the ethical implications of moderation.
3 tradeoffs and 3 stress tests locked
Interview prep
Unlock the interviewer X-Ray rubric, tradeoff matrix, stress tests, clarifying questions, and key concepts.
Investigate Harassment Surge and Mitigation
Problem: Investigating a Harassment Surge
Suppose your monthly violation analysis, using consistent moderation settings, indicates a sharp increase in "Harassment" violations during the most recent month.
Discuss the following aspects of this situation:
1. Plausible Explanations for the Surge
Identify the most plausible explanations for this surge. Consider both real-world behavioral changes and measurement-related artifacts. Examples include:
- A genuine increase in abusive behavior.
- Changes in traffic mix across different surfaces, regions, languages, or creator cohorts.
- Seasonality or external events influencing user behavior.
- Coordinated attacks or the activity of repeat offenders.
- Changes in policy definitions.
- Adjustments to model thresholds or the deployment of new model versions.
- Calibration drift in the underlying classification model.
- Issues related to data quality, logging, or data backfills.
2. Investigation Strategy
Detail how you would investigate whether the surge is a real phenomenon or an artifact of the measurement system. Be specific about:
- The prevalence metrics you would employ.
- The appropriate denominators and time windows for comparison.
- The segments you would use to break down the data for deeper analysis.
- Additional datasets you would request, such as human-review labels, user reports, enforcement logs, or model-version metadata.
- Methods to account for confounding variables, selection bias, and Simpson's paradox.
3. Proposed Solutions (If Surge is Real)
If the investigation confirms the surge is real, propose a range of solutions. These should encompass product, policy, ranking, operational, and machine learning interventions. Include both short-term containment actions and longer-term strategic fixes.
4. Evaluation of Solutions
Describe how you would evaluate the effectiveness of your proposed solutions. This should cover:
- Primary and guardrail metrics.
- Experiment or rollout design methodologies.
- Key tradeoffs, specifically considering false positives, fairness, and overall user experience.
Throughout your discussion, ensure you distinguish between a volume increase and a rate increase in violations. Additionally, explain how you would validate model-driven metrics using human-reviewed labels.