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Design Metrics for Content Moderation and Chatbot Evaluation

TikTokData Scientist · Technical Screen
MediumData ScientistTechnical ScreenAnalytics & Experimentation

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This interview question evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer for Design Metrics for Content Moderation and Chatbot Evaluation states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

Scenario

Trust & Safety data science: design metrics for a content-moderation A/B test and for evaluating a customer-service chatbot’s knowledge base.

Question

In a content-moderation A/B test where harmful-content prevalence is low, which short-term, user-centric metrics would you track to detect impact quickly and why? How would you design an experiment and select evaluation metrics to measure the quality and usefulness of a customer-service chatbot’s knowledge base?

Hints

Consider immediate user actions: report rates, dismissals, session exits, latency; for chatbot, precision/recall of answers, deflection rate, CSAT; discuss experiment design and trade-offs.