How would you evaluate stolen-post detection?
The product team is launching a new Stolen Post Detection algorithm designed to flag posts suspected of being copied or reposted without attribution. Upon detection, the system will trigger various enforcement actions, such as downranking the post, applying a warning label, notifying the original creator, or removing the post entirely.
Your task is to design a comprehensive evaluation plan for this new algorithm, covering the following key areas:
1. Problem Diagnosis & Clarification
What questions would you ask to clarify the product goal and the precise definition of “stolen” content (e.g., distinguishing between exact duplicates, paraphrased content, or the use of meme templates)? Additionally, how would you clarify the intended enforcement actions and the overall success criteria for this initiative?
2. Harms & Tradeoffs
Enumerate the likely failure modes and potential harms associated with both false positives and false negatives. Consider the varying impacts these failures could have on different stakeholders, including the original creator, the reposter, general viewers, and content moderators.
3. Metrics
Propose a robust metric framework. This framework should include:
- Primary success metrics.
- Guardrail metrics.
- Offline model metrics. Ensure your framework includes at least one metric that can exhibit inverse behavior depending on the chosen detection threshold.
4. Experiment Design
Propose an online experiment (or a quasi-experiment if a pure A/B test proves difficult) to evaluate the algorithm. Your design should specifically address:
- Logging requirements.
- The appropriate unit of randomization.
- Potential interference or network effects.
- A strategic ramp-up plan.
- How you would compute and interpret statistical power and Minimum Detectable Effect (MDE).
5. Post-Launch Monitoring
Outline what you would monitor after launch to detect regressions or instances of users attempting to game the system. Describe your strategy for iterating on detection thresholds and overall policy over time.
AI interview prep
Core dilemma
Balancing the aggressive pursuit of content originality (high recall) against the risk of penalizing legitimate or benign content (false positives, user friction), while navigating complex definitions of "stolen" and potential network effects on a large-scale social platform.
3 tradeoffs and 3 stress tests locked
Interview prep
Unlock the interviewer X-Ray rubric, tradeoff matrix, stress tests, clarifying questions, and key concepts.
How would you evaluate stolen-post detection?
The product team is launching a new Stolen Post Detection algorithm designed to flag posts suspected of being copied or reposted without attribution. Upon detection, the system will trigger various enforcement actions, such as downranking the post, applying a warning label, notifying the original creator, or removing the post entirely.
Your task is to design a comprehensive evaluation plan for this new algorithm, covering the following key areas:
1. Problem Diagnosis & Clarification
What questions would you ask to clarify the product goal and the precise definition of “stolen” content (e.g., distinguishing between exact duplicates, paraphrased content, or the use of meme templates)? Additionally, how would you clarify the intended enforcement actions and the overall success criteria for this initiative?
2. Harms & Tradeoffs
Enumerate the likely failure modes and potential harms associated with both false positives and false negatives. Consider the varying impacts these failures could have on different stakeholders, including the original creator, the reposter, general viewers, and content moderators.
3. Metrics
Propose a robust metric framework. This framework should include:
- Primary success metrics.
- Guardrail metrics.
- Offline model metrics. Ensure your framework includes at least one metric that can exhibit inverse behavior depending on the chosen detection threshold.
4. Experiment Design
Propose an online experiment (or a quasi-experiment if a pure A/B test proves difficult) to evaluate the algorithm. Your design should specifically address:
- Logging requirements.
- The appropriate unit of randomization.
- Potential interference or network effects.
- A strategic ramp-up plan.
- How you would compute and interpret statistical power and Minimum Detectable Effect (MDE).
5. Post-Launch Monitoring
Outline what you would monitor after launch to detect regressions or instances of users attempting to game the system. Describe your strategy for iterating on detection thresholds and overall policy over time.