Project Overview
In an age of strict data privacy laws (like GDPR) and heightened brand sensitivity, publishers and platforms face the immense challenge of manually reviewing all submitted content. This AI-powered content moderation tool was designed to solve this exact problem. It acts as an intelligent first line of defense, automatically auditing articles, user submissions, and even PDF documents before they go public.
The system uses advanced Natural Language Processing (NLP) to scan text for a wide range of sensitive information. This includes personally identifiable information (PII) like names and phone numbers, as well as violent language, discriminatory terms, or other content that could pose a legal or reputational risk. Any detected issues are immediately highlighted and flagged for review, allowing human editors to focus their time on the content that truly needs attention. This automated audit trail, secured with role-based access, ensures compliance, protects user data, and maintains brand safety at scale.
Application Showcase
Key Features
- Efficient Content Processing: Handles uploads of news content, including PDF documents.
- AI-Powered Analysis: Identifies sentiment, key individuals, and potential reputational risks.
- Automated Tagging: Classifies content to surface relevant insights automatically.
- Moderation Interface: Allows for easy review and refinement of AI-generated results.
- Secure Role-Based Access: Provides secure access for analysts and moderators via AWS Cognito.
- Comprehensive Audit Trails: Logs all changes and user actions for full transparency.
Solutions
- Decoupled the upload system with asynchronous processing using AWS S3 and Lambda.
- Implemented batch processing and page limitations to maintain performance.
- Applied an active learning pipeline with a feedback loop to retrain and improve the model.