Product design question

Design a trust-and-safety system for models and datasets uploaded by the community.

Practice this question out loud. An AI interviewer asks it, follows up like a real interviewer would, and scores your answer. Type or speak.

What this question tests

Product design for trust and safety at scale, balancing openness with real risks like malicious model weights or unsafe datasets.

Clarify the threat model: malicious code embedded in model files, harmful or non-consensual content in datasets, and license violations.
Segment enforcement by risk tier: automated scanning for all uploads, and deeper review triggered by popularity or user reports.
Propose the pipeline: automated malware and pickle-exploit scanning on upload, a visible safety badge, and a fast community-reporting flow.
Design the response layer: quarantine suspicious uploads pending review rather than instant takedown, to avoid over-blocking legitimate research.
Define success: reduction in time-to-detection for harmful uploads and low false-positive rate on quarantines.

Names a real known risk specifically, like unsafe pickle deserialization in model files, and proposes automated scanning as the first line of defense.
Separates automated pre-upload checks from post-upload community reporting, since neither alone catches everything at this scale.
Proposes a graduated response, quarantine and review rather than instant deletion, to protect legitimate researchers from false positives.
Assumes a candidate metric, like median time from upload to detection of malicious content under 24 hours, marked as an assumption.

Proposing manual human review of every upload, which cannot scale to millions of models and datasets.
Ignoring the specific technical risk of executable code inside model files, treating this like a generic content moderation problem.

Chapters of the AI PM course that teach what this question tests.