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This is a hard difficulty Machine Learning question, commonly asked during Technical Screen rounds at Google.

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This question is commonly asked for Data Scientist candidates at Google during technical interviews.

Build and evaluate illegal-video classifier

Quick Overview

This question evaluates competency in end-to-end Machine Learning system design, including multimodal modeling (vision, audio, text), data engineering for sparse, noisy, and imbalanced labels, robustness and abuse resistance, human-in-the-loop workflows, privacy/retention concerns, and operational metrics.

End-to-End ML System Design: Flag Illegal YouTube Videos

You are tasked with designing a production ML system to detect and triage potentially illegal YouTube videos at scale. The system must work across modalities (vision, audio, text), handle sparse/noisy labels, strong class imbalance, evolving policies, and integrate with human review.

Assumptions (make minimal, explicit):

"Illegal" follows platform policy (e.g., child safety, terror content, incitement to violence), with versioned policy definitions that evolve over time.
Actions include: automatic block, downrank/age-restrict, route to human review, or allow.
The system must support multilingual/global content and near-real-time decisions.

Design the system across the following areas:

1) Data

Inputs: video frames/thumbnails, audio tracks, ASR captions/transcripts, titles/descriptions/tags, uploader/channel metadata, user flags, policy takedown logs.
Constraints: sparse and noisy labels, severe class imbalance, evolving policies.
Describe data ingestion, feature storage, deduplication/near-duplicate handling, label pipelines (including policy-version tracking), and privacy/retention considerations.

2) Modeling

Choose architectures per modality (vision, audio, text) and a multimodal fusion approach.
Pretraining/embeddings strategy (self-supervised/foundation models; multilingual coverage).
Strategy for weak supervision (heuristics, user flags, external lists) and active learning to acquire high-value labels.
Handling class imbalance, noisy labels, and continual learning under policy drift.

3) Evaluation

Offline metrics: AUROC, PR-AUC (class imbalance), calibration (ECE/Brier), and cost-weighted utility.
Thresholding for triage tiers (auto-block, send-to-review, allow), grounded in expected utility and reviewer capacity.
Build a reliable test set that resists leakage, near-duplicates, and distribution shift; include slice-based evaluation (language, region, topic, channel age).

4) Safety and Abuse Resistance

Anticipate adversarial evasion and propose robustification and monitoring (without revealing evasion recipes).
Fairness and false-positive harm mitigation; transparent appeals workflow; reversibility of actions.
Human-in-the-loop design: reviewer tooling, quality control, throughput/SLA constraints, and prioritization.

5) Online Rollout and Measurement

Rollout plan: shadow mode, canary, progressive ramp, and interleaving with existing human/rule systems; kill switches.
Counterfactual risk estimation using IPS/DR to estimate violation risks and action costs offline.
Experiment design to measure reduction in policy violations without selection bias; randomized auditing to estimate true prevalence.

Quick Overview

End-to-End ML System Design: Flag Illegal YouTube Videos

Assumptions (make minimal, explicit):

"Illegal" follows platform policy (e.g., child safety, terror content, incitement to violence), with versioned policy definitions that evolve over time.

Actions include: automatic block, downrank/age-restrict, route to human review, or allow.

The system must support multilingual/global content and near-real-time decisions.

Design the system across the following areas:

1) Data

Inputs: video frames/thumbnails, audio tracks, ASR captions/transcripts, titles/descriptions/tags, uploader/channel metadata, user flags, policy takedown logs.

Constraints: sparse and noisy labels, severe class imbalance, evolving policies.

Describe data ingestion, feature storage, deduplication/near-duplicate handling, label pipelines (including policy-version tracking), and privacy/retention considerations.

2) Modeling

Choose architectures per modality (vision, audio, text) and a multimodal fusion approach.

Pretraining/embeddings strategy (self-supervised/foundation models; multilingual coverage).

Strategy for weak supervision (heuristics, user flags, external lists) and active learning to acquire high-value labels.

Handling class imbalance, noisy labels, and continual learning under policy drift.

3) Evaluation

Offline metrics: AUROC, PR-AUC (class imbalance), calibration (ECE/Brier), and cost-weighted utility.

Thresholding for triage tiers (auto-block, send-to-review, allow), grounded in expected utility and reviewer capacity.

Build a reliable test set that resists leakage, near-duplicates, and distribution shift; include slice-based evaluation (language, region, topic, channel age).

4) Safety and Abuse Resistance

Anticipate adversarial evasion and propose robustification and monitoring (without revealing evasion recipes).

Fairness and false-positive harm mitigation; transparent appeals workflow; reversibility of actions.

Human-in-the-loop design: reviewer tooling, quality control, throughput/SLA constraints, and prioritization.

5) Online Rollout and Measurement

Rollout plan: shadow mode, canary, progressive ramp, and interleaving with existing human/rule systems; kill switches.

Counterfactual risk estimation using IPS/DR to estimate violation risks and action costs offline.

Experiment design to measure reduction in policy violations without selection bias; randomized auditing to estimate true prevalence.

Build and evaluate illegal-video classifier

Quick Overview

End-to-End ML System Design: Flag Illegal YouTube Videos

1) Data

2) Modeling

3) Evaluation

4) Safety and Abuse Resistance

5) Online Rollout and Measurement

Solution

Comments (0)

Build and evaluate illegal-video classifier

Quick Overview

End-to-End ML System Design: Flag Illegal YouTube Videos

1) Data

2) Modeling

3) Evaluation

4) Safety and Abuse Resistance

5) Online Rollout and Measurement

Solution

Comments (0)