AI Content
Quality
Monitoring
A monitoring framework for AI-assisted content moderation quality, model risk, and human review workload.
AI moderation does
not fail in one way.
A missed threat creates a safety risk. A false flag creates a poor user experience. Both matter, but they do not have the same operational cost. This framework sits between an AI classifier and a human review team to monitor moderation quality, review workload, and model risk.
Risk was not evenly distributed
Most comments were safe, but rare categories like threat and identity hate carried higher review risk.
Model quality varied by label
Threat and identity_hate were the weakest labels by F1 because they had less training signal.
Thresholds changed workload
Changing confidence thresholds shifted the balance between safety coverage and human review volume.
Review routing mattered
The framework separated auto-approve, human review, and escalation decisions.
Priority scoring improved triage
A final priority score combined severity, multi-label toxicity, and model uncertainty.
Dashboard outputs made monitoring repeatable
The pipeline produced Power BI-ready tables for quality, workload, and threshold tracking.
Four layers, applied in sequence.
Data preparation
Processed comments, created label features, risk scores, and risk tiers.
Baseline model
TF-IDF + OneVsRest Logistic Regression baseline to generate per-label probabilities.
Routing logic
Converted model scores into auto-approve, human review, and escalation decisions.
Monitoring outputs
Created dashboard-ready tables for model performance, review queue, threshold scenarios, and workload monitoring.
What the monitoring
system showed.
Dataset is heavily skewed toward safe content. Rare labels require separate monitoring.
80% auto-approved keeps reviewer workload manageable while surfacing high-risk items.
Low F1 on high-risk labels means these categories need human review regardless of score.
Each threshold scenario was modeled to show workload and residual risk before any policy change.
Route risk,
not just scores.
The model output becomes useful only when it is translated into operational decisions. High-confidence safe content can be auto-approved, uncertain content should go to review, and high-risk labels should be escalated even when they are rare.
A clearer view of quality,
risk, and workload.
The framework gives Trust & Safety or AI Operations teams a practical way to monitor quality, prioritize review, and understand how threshold choices affect workload and residual risk.
The value is not the classifier alone. It is the control layer around it.
Monitor the system,
not just the model.
Track performance by label
Rare labels should be monitored separately because aggregate metrics hide risk.
Keep high-risk labels in human review
Threat and identity_hate should not rely on automatic action alone.
Use threshold scenarios before changing policy
Every threshold change should show the workload and residual risk trade-off.
Prioritize review by risk and uncertainty
Human reviewers should see the riskiest and most uncertain comments first.
Create recurring dashboard outputs
Teams need repeatable monitoring tables, not one-off model evaluation.
What I owned.
I structured the monitoring framework, prepared the dataset, trained a baseline classifier, designed routing rules, built the priority scoring logic, created threshold scenario analysis, and generated dashboard-ready outputs for moderation quality and review workload.
Explore the rest of the work.
The repository includes data preparation, classifier training, routing logic, priority scoring, threshold scenario analysis, and dashboard-ready monitoring outputs.