Toxic Content Classifier

— ML System Design

fig 1.

Project Scope

Business objective:

The goal is to build a system that detects harmful content in user inputs to enhance trust and ensure a positive user experience. From a business perspective, the aim is to improve user confidence by promoting fairness, avoiding toxicity, and aligning with human values, thereby encouraging continued use of the product.

Toxic content classifiers can be integrated with any social media application.

Clarifying questions to ask:

1. Upstream/Downstream Tasks: Clarify if there are domain-specific requirements or dependencies to consider while designing the system.
2. Real-Time Constraints: Determine if the system needs to work in real-time, requiring low latency and potentially smaller model sizes.
3. Dataset Availability: Confirm the existence of labeled datasets, including toxic and non-toxic content, derived from user feedback.
4. Baseline Systems: Check for any existing systems or business/offline metrics to serve as a baseline for improvements.

Functional Requirements:

The ML system should enhance user engagement, reducing user complaints, and minimizing customer service involvement.

Non-Functional Requirements:

1. Scalability: Handle billions of users with low latency, and real-time performance.
2. Availability: Ensure high system uptime.
3. Computational Resources: Optimize resource use (GPUs, CPUs) and balance model size with data size within the computation budget.
4. Analytics: Enable insights into trending posts, political activities, riots, and protests.
5. Tooling: System monitoring, Debugging tools, and Alerts for production issues, Research experiment tracking and model registries via tools like Weights & Biases or MLflow.

Offline & Online Metrics

Offline Metrics: Accuracy, Precision, Recall, F1-Score, ROC_AUC, PR_AUC

Online Metrics: Measure real-world system performance, balancing business goals and user experience.

1. False Positives (FPs): Minimize FPs to reduce customer service (CS) workload and operational costs.
2. False Negatives (FNs): Minimize FNs to catch as much toxic content as possible.
3. CS Team Intervention: Reduce intervention rates, reflecting improved automation and accuracy.
4. User Engagement: Retention rate and content interaction statistics post-moderation.
5. System Performance Metrics: Latency, Throughput, Cost per Moderated Content.

Mental Model:

fig 2.

Data Pipeline:

1. Data Collection:

• Leverage user feedback (e.g., reports reviewed by CS team) for labeled toxic and non-toxic data.
• Use LLMs to pre-label data with context and rules, followed by annotators review for accuracy.
• Explore open-source datasets (e.g., Common Crawl, Wikipedia) for additional domain-specific data.
• Data types include text data (books having harmful & non-harmful content, News articles, Criminal activity articles) and user posts.

2. Feature Involvement:

• Include features such as user behavior (e.g., past activity), demographics, geography, post content, and post quality for analytics and insights.

3. Data Preprocessing:

• Perform noise reduction and normalize or scale numerical data.
• Use text preprocessing techniques like punctuation stripping, stopword removal, stemming, and lemmatization (e.g., via NLTK).
• Employ tokenization methods: Word Tokenization: Split text into words, Subword Tokenization: Split words into subword units (e.g., BPE), Byte-Level Tokenization: Byte-level representation (e.g., GPT models).
• Generate embeddings using methods like TF-IDF, Word2Vec, or contextual models (e.g., BERT, RoBERTa).
• Apply dimensionality reduction (PCA, t-SNE) to remove correlated features and retain essential information, if required.

4. Data Splitting:

• Split into training, validation, and test sets (70%, 15%, 15%).
• Simulate real-world scenarios (e.g., training data = 3 weeks, validation = 1 week).

5. Data Balancing:

• Address class imbalance with techniques like oversampling, undersampling, or synthetic augmentation.

6. Synthetic Data Augmentation:

• Use LLMs to generate new samples while preserving context.
• Validate augmented data consistency with another LLM.

7. Data Consistency and Distribution Analysis:

• Check for biases (e.g., underrepresented groups) and ensure sufficient diversity.
• Analyze data distribution and detect patterns for alignment with real-world scenarios.
• Ensure there is no data leakage.

8. Data Privacy and Security:

• Ensure anonymization of PII and compliance with data regulations like GDPR and CCPA.

9. Pipeline Automation:

• Implement an ETL process for efficient handling of new data, automating extraction, transformation, and loading tasks.

Model Pipeline

FIG 3.

1. Initial Setup:

• Baselines: Start with simple baselines like logistic regression, Random forest, or open-source benchmarks for quick iterations.

2. Embedding Generation:

• Options range from a bag of words to contextual embeddings like BERT, RoBERTa, or smaller, pre-trained transformer models like DistillBERT or Tiny Transformer (compact 2M param model).

3. Model Selection:

Classical Models:

• Logistic Regression, Random Forest, GBDT for simplicity and interpretability.

Deep Learning:

• RNNs/LSTMs for contextual understanding but slower with long dependencies.
• Transformers (BERT, RoBERTa, DistillBERT): For efficient and accurate contextual analysis.

Compact Models:

• Pre-train compact models on large corpora and fine-tune them on the toxic dataset.

4. Advanced Techniques for Text:

• Handle other modalities (video, images, audio) with tools like OCR for extracting text.
• We can also use MultiModals like Gemini, when dealing with other modalities (video, images, audio).
• Fine-tune advanced LLMs (e.g., LLAMA, Qwen2) with parameter-efficient tuning (LoRA, QLoRA).

5. Model Optimization:

• Loss Function: Binary Cross-Entropy (BCE) for toxic classification.
• Optimizer: Adam for faster convergence.
• Overfitting/Underfitting Management: Regularization, dropout, and careful analysis of training/validation metrics.

6. Iterative Improvement:

• Error Analysis: Analyze false positives/negatives for data or model improvement.
• Hyperparameter Tuning: Use tools like Hydra or Optuna for efficient searches.

7. Model Compression:

• Techniques: Pruning, quantization (post-training or QAT), and knowledge distillation.
• Reference: Use tools like DistillKit for small, efficient models.

8. System Extensions:

• Filter violent/hate groups, avoid recommending harmful content, and prioritize moderation based on spread likelihood.
• Challenges include evolving harmful content, class imbalance, and balancing model size, latency, and performance.

9. Distributed Training:

• Use frameworks like DDP or ZeRO for training large-scale models effectively.

10. Ethical and Practical Considerations:

• Ensure compliance with content policies and implement warning systems for users posting harmful content.

This pipeline balances simplicity, scalability, and effectiveness while addressing practical challenges in moderating harmful content.

Model Evaluation

1. Offline Evaluation:

Goal: Assess model performance from a research standpoint using test data that resembles the production environment.

Process:

• Use historical and real-time data in the evaluation set.
• Compute metrics (e.g., Precision, Recall, F1-score) after training and validation.
• Focus on offline evaluation to ensure readiness for online deployment.

2. Online Evaluation:

Goal: Evaluate the model’s business impact in the live environment.

Process:

• Conduct A/B testing after achieving satisfactory offline metrics.
• Compare the performance of the new model with the existing system, factoring in complexity.

3. A/B Testing:

Approach:

• Divide users into two groups: one served by the old model and the other by the new model.
• Measure performance through statistical significance testing.
• Null Hypothesis: The new model shows no significant improvement.
• Compute p-value using hypothesis tests (e.g., G-test, Z-test). Reject the null hypothesis if p≤0.05p \leq 0.05p≤0.05, indicating significant improvement.

Alternatives for New Systems:

• Use a Holdout Set for comparisons.
• Perform A/A Testing to ensure consistency across user groups.

4. Multi-Armed Bandit Testing:

Key Strategies:

• Exploration: Try different models or configurations.
• Exploitation: Prioritize the best-performing model.
• E-Greedy: Balance exploration and exploitation.
• Zero-Regret Strategy: Minimize losses during exploration.
• UCB (Upper Confidence Bound): Choose actions with high confidence bounds.

5. Key Considerations and Tradeoffs:

Model Properties:

• Robustness: Test performance with noisy inputs.
• Fairness: Ensure no biases toward specific user groups or demographics.

Cost-Benefit Analysis:

• Deploy only if the engagement gain is statistically significant and worth the added complexity.

Conclusion:

Deploy the new model if A/B testing demonstrates statistically significant engagement gains and the added complexity is justified. Otherwise, refine the model or retain the existing system.

Model Deployment

1. Deployment Modes:

Batch Mode:

• Process large quantities of data at intervals (e.g., daily or weekly).
• Resource-efficient and scalable, suitable when latency isn’t critical.

Real-Time Mode:

• Offers low latency and up-to-date moderation but requires higher resources and adds complexity.
• Preparedness for handling errors, updates, and changing user behaviors is essential.

2. Key Questions to Address:

Where to Deploy (User Device vs. Server):

• User Device: Enhances privacy, avoids server costs but is constrained by device resources and challenging updates.
• Server-Based: Centralized updates, scalable, and suitable for dynamic environments but involves higher operational costs.

Batch Mode vs. Real-Time:

• Batch: High efficiency and scalability, but with latency in moderation and potential outdated recommendations.
• Real-Time: Better user experience with immediate feedback but more complex and resource-intensive.
• Hybrid Approach: Combine batch processing (e.g., offline candidate generation) with real-time updates for scalability and efficiency.

Deployment Scope:

• Deploy to a fraction of users first for monitoring and gradual rollout.
• Shadow Deployment: Monitor outputs without influencing user experience.
• Replace the existing system with gradual ramp-up and rollback options.

3. Deployment Strategies:

Canary Deployment:

• Rollout to a small traffic fraction, monitor system performance, and gradually increase.
• Early-stage problem detection with minimal impact.

Blue-Green Deployment:

• Switch between old and new systems using a router for seamless rollbacks if issues arise.

4. Deployment Preconditions:

Assets Required for Safe Deployment:

• End-to-end test set defining inputs/outputs.
• Confidence test set for performance metric evaluation.
• Clear performance metric and acceptable value range.

5. Best Practices:

• Use gradual traffic allocation to detect issues early.
• Monitor resource utilization, latency, and accuracy in production.
• Ensure rollback mechanisms are in place for reliability.

This systematic approach balances efficiency, scalability, and risk mitigation in deploying a toxic content classifier ML System.

Model Serving & Monitoring

1. Gradual Adaptation for Users:

• Introduce new systems gradually to allow users to adapt to changes in interactions.

2. Data Privacy and Security:

• Ensure compliance with regulations (e.g., GDPR) and safeguard user data against breaches.

3. Logging and Analytics:

• Log errors, user interactions, and suspicious activities for offline analysis to enhance system performance.
• Measure user engagement (e.g., click-through rate, daily active users) to monitor business impact.

4. Monitoring Framework:

• Software Metrics: Track latency, memory, compute usage, and server load.
• Business Metrics: Monitor user engagement, CS team flags, and retention (e.g., DAU, sessions).
• Model Metrics: Regularly evaluate performance on a confidence test set, performance test set, or end-to-end test set.
• Update test sets to reflect current data and avoid distribution shifts.
• Check for prediction bias and changes in data label distribution over time.

5. Input Feature Monitoring:

• Detect missing, null, or corrupted values in input features.
• Monitor feature distributions using statistical tests (e.g., Chi-square test) to identify significant shifts.

6. Numerical Stability:

• Set alerts for unstable computations (e.g., NaNs or null values in predictions).

7. Adaptive Thresholds:

• Define dynamic thresholds for metrics (e.g., server load during peak hours) and adjust them as requirements evolve.

8. Concept Drift:

• Monitor for changes in business or conceptual requirements.
• Retrain the model with updated requirements to maintain relevance.

9. Data Drift:

• Track user behavior changes.
• Recalibrate the model as necessary to account for these shifts.

Key Takeaways:

• Comprehensive monitoring ensures the model’s robustness, fairness, and reliability.
• Regular updates to evaluation datasets and thresholds help mitigate distribution shifts and evolving requirements.
• A proactive approach to logging, monitoring, and alerts ensures long-term system performance and user satisfaction.

Conclusion:

Building an ML system for toxic content classification is a complex, iterative process that requires careful attention to data quality, model performance, and deployment strategies.

Success relies on creating a robust data pipeline to ensure diverse, representative, and high-quality data, alongside a well-tuned model pipeline that balances accuracy, fairness, and efficiency. Continuous monitoring and evaluation are crucial to address evolving user behavior, data drift, and conceptual shifts.

By iteratively improving data and model pipelines, leveraging advanced techniques like fine-tuning and model compression, and ensuring ethical considerations such as privacy and fairness, the system can effectively detect toxic content.

With proper deployment and monitoring strategies, the classifier can maintain high performance while adapting to dynamic environments, ensuring a safer and more engaging user experience.