walkthrough.md

🛡️ SocialStreamModerationEnv Project Completion Walkthrough

This document outlines the final architecture, implementation phases, and deliverables for the AI Social Media Policy Sandbox. We have successfully built a sophisticated, API-first OpenEnv environment that enables researchers to evaluate AI moderators on nuanced social media policy decisions.

🧩 Core Architecture

The environment is structured in a modular fashion to ensure scalability and ease of extension:

• envs/social_stream_moderation/: The core package containing the main environment logic, task configurations, data models, and the reward engine.
• scripts/: Includes the synthetic data generator that populates the environment with realistic (yet safe) edge cases like sarcasm and quoted condemnation of hate speech.
• app.py & inference.py: The interface layer. app.py provides a FastAPI wrapper for remote interaction, while inference.py serves as the CLI for local evaluations and baseline agents.

✅ Key Deliverables

• Deterministic Rewards: A granular reward matrix that balances harm prevention against censorship.
• Fairness Grader: Automatic evaluation of disparate impacts across user groups.
• OpenEnv Compliance: Standardized /reset, /step, and /state API endpoints.
• Baseline Agents: Both rule-based and LLM-capable moderation policies included.
• Deployment Ready: Docker-optimized with all dependencies and metadata files (openenv.yaml) included.

📊 Verification Results (Local Runs)

All tasks have been successfully verified with our rule-based agent:

• Easy Task: Perfect score (1.0).
• Medium Task: Excellent score (~0.96) handling context and nuance.
• Hard Task: High score (0.99) while maintaining fairness constraints.

🚀 Future Outlook

This product is ready to be used by Trust & Safety teams to:

1. Benchmark existing LLM-based moderators.
2. Experiment with different "Brand Safety" modes (Lenient vs. Strict).
3. Test if agents can be "fair" across demographic user groups.