🔗 Live Demo: https://ai-review-moderation-2-hdqedtqcmlbagcmiewmebt.streamlit.app

📂 GitHub: https://github.com/itingtseng/ai-review-moderation-2

Content moderation is often treated as a classification problem, but in reality, it is a decision-making problem under uncertainty.

In high-volume moderation environments, even small inefficiencies can scale into significant operational cost and inconsistency. Moderators must make fast decisions on ambiguous content, often without sufficient context or clear justification, leading to slower reviews, inconsistent outcomes, and reduced trust in both the system and the decisions themselves.

This project explores how to design a human-in-the-loop moderation system that improves decision confidence, consistency, and speed by shifting from model-driven outputs to an evidence-first decision experience.

TL;DR

• What: Hybrid moderation system (rules + similar cases)
• Why: Improve decision confidence under uncertainty
• How: Evidence-first interface + human-in-the-loop design
• Impact: Faster reviews, more consistent decisions, reduced escalation

Context

Content moderators review hundreds of borderline cases daily under time pressure and policy ambiguity.

Despite existing tools, decisions are often:

• Inconsistent across reviewers
• Over-reliant on personal judgment
• Difficult to audit or justify