Design a harmful content detection system
Problem Description
Design an end-to-end harmful content detection system for a large, mobile-first, ephemeral, consumer social platform with a global user base, including teens. The platform supports multimodal content across various surfaces, such as 1:1/group chat, short-form video, stories, lenses/AR effects, and live audio/video. The system must detect and act on harmful content in near real-time while respecting user privacy, regional policies, and user trust.
Core Requirements
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Taxonomy and Labeling:
- Define a practical taxonomy for harmful content (e.g., hate, self-harm, sexual content, violence), including subcategories and severity tiers.
- Establish clear labeling guidelines and quality controls for content annotation.
- Define the multilingual and multimodal scope (text, image, audio, video) and context rules (e.g., multi-turn chat, text-in-image).
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Modeling and Data Strategy:
- Propose suitable model choices, ranging from keyword baselines and classical machine learning to modern transformers and multimodal encoders.
- Outline a comprehensive training data strategy, covering data collection, active learning, long-tail sampling, debiasing techniques, and handling sensitive content classes.
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Inference and Decisioning:
- Design the inference architecture, considering streaming vs. batch processing, on-device vs. server-side execution, and defining latency targets for different content surfaces.
- Establish thresholding mechanisms, severity tiers, and policy actions (e.g., block, age-gate, interstitial, downrank, quarantine).
- Incorporate a human-in-the-loop system, including triage queues, escalation paths, and appeal/override flows.
- Address explainability requirements for both human moderators and user-facing transparency.
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Risk, Privacy, and Reliability:
- Mitigate adversarial behavior, such as evasion techniques (obfuscation, text-in-image, coded language), prompt injection (for any LLM components), and implement model hardening strategies.
- Adhere to privacy and safety constraints, including data minimization, retention policies, encryption, and age-appropriate design principles.
- Manage fairness and error costs, explicitly considering precision/recall trade-offs by content class and region, as well as group fairness.
- Implement robust monitoring, drift detection, A/B rollout mechanisms, and feedback loops for continuous system improvement.
Deliverable
Provide a cohesive design that integrates all these components into an operational system, clearly explaining your assumptions and the trade-offs made.
AI interview prep
Core dilemma
Balancing near real-time, high-recall detection of diverse, adversarial harmful content across multimodal, multilingual data at massive scale, against strict requirements for precision, fairness, privacy, and operational efficiency.
3 tradeoffs and 3 stress tests locked
Interview prep
Unlock the interviewer X-Ray rubric, tradeoff matrix, stress tests, clarifying questions, and key concepts.
Design a harmful content detection system
Problem Description
Design an end-to-end harmful content detection system for a large, mobile-first, ephemeral, consumer social platform with a global user base, including teens. The platform supports multimodal content across various surfaces, such as 1:1/group chat, short-form video, stories, lenses/AR effects, and live audio/video. The system must detect and act on harmful content in near real-time while respecting user privacy, regional policies, and user trust.
Core Requirements
-
Taxonomy and Labeling:
- Define a practical taxonomy for harmful content (e.g., hate, self-harm, sexual content, violence), including subcategories and severity tiers.
- Establish clear labeling guidelines and quality controls for content annotation.
- Define the multilingual and multimodal scope (text, image, audio, video) and context rules (e.g., multi-turn chat, text-in-image).
-
Modeling and Data Strategy:
- Propose suitable model choices, ranging from keyword baselines and classical machine learning to modern transformers and multimodal encoders.
- Outline a comprehensive training data strategy, covering data collection, active learning, long-tail sampling, debiasing techniques, and handling sensitive content classes.
-
Inference and Decisioning:
- Design the inference architecture, considering streaming vs. batch processing, on-device vs. server-side execution, and defining latency targets for different content surfaces.
- Establish thresholding mechanisms, severity tiers, and policy actions (e.g., block, age-gate, interstitial, downrank, quarantine).
- Incorporate a human-in-the-loop system, including triage queues, escalation paths, and appeal/override flows.
- Address explainability requirements for both human moderators and user-facing transparency.
-
Risk, Privacy, and Reliability:
- Mitigate adversarial behavior, such as evasion techniques (obfuscation, text-in-image, coded language), prompt injection (for any LLM components), and implement model hardening strategies.
- Adhere to privacy and safety constraints, including data minimization, retention policies, encryption, and age-appropriate design principles.
- Manage fairness and error costs, explicitly considering precision/recall trade-offs by content class and region, as well as group fairness.
- Implement robust monitoring, drift detection, A/B rollout mechanisms, and feedback loops for continuous system improvement.
Deliverable
Provide a cohesive design that integrates all these components into an operational system, clearly explaining your assumptions and the trade-offs made.