Content Moderation

AI-powered detection and removal of harmful user-generated content to ensure platform safety.

Business impact

• content removal rate — Higher rate of harmful content removal improves platform safety and compliance
• detection accuracy — More accurate identification reduces false positives and negatives in moderation
• user safety — Safer environment increases user trust and engagement on the platform
• policy enforcement — Consistent enforcement ensures adherence to community standards and regulations
• content moderation efficiency — Automation speeds up review processes and reduces human workload
• user trust — Reliable moderation fosters confidence in platform integrity and fairness
• operational efficiency — Streamlined workflows lower costs and improve scalability of moderation efforts
• compliance with regulations — Meeting legal requirements avoids fines and reputational damage

Data requirements

• user-generated text content (Text) — Analyzed for hate speech, misinformation, and policy violations
• images and videos (Image) — Processed to detect violent, explicit, or prohibited visual content
• audio streams (Audio) — Transcribed and analyzed for harmful speech or sounds
• metadata and user reports (Structured) — Used to prioritize and contextualize moderation decisions
• platform interaction logs (Numeric) — Help identify patterns of harmful behavior and content spread

AI methods and techniques

• Predictive AI — Predicts likelihood of content violating policies for proactive moderation
• Generative AI — Generates explanations and context for flagged content to assist reviewers
• Symbolic AI — Applies rule-based logic to enforce explicit content policies and guidelines

AI models and model families

GPT-4, LLaMA, Claude, Vision-Language Models (VLMs), Large Language Models (LLMs)

Industries

Real-world evidence

8 documented case studies on record.

Companies using this: Gumtree, Meta, Meta Platforms Inc, Provenance AI, Sama, Telus, Tik Tok Byte Dance Ltd, You Tube.