AI Content Evaluation/Data Labeling for AI-Powered Filtering Extension
Developed and implemented natural-language-based filtering and evaluation systems for AI-powered browser extension. Classified social media content based on user intent, preference alignment, and quality assessment to enhance personalized content curation. Evaluated AI model outputs to determine content visibility, quality, and adherence to user-described preferences. • Filtered and rated social media and online content according to natural-language user profiles. • Identified and labeled misleading, unwanted, and low-quality recommendations in feed streams. • Assessed and tagged creator-inserted advertisements, sponsorships, and behavioral patterns. • Utilized scoring and preference rules to align content feeds with explicit user directions.