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
- [x] **Deterministic Rewards:** A granular reward matrix that balances harm prevention against censorship.
- [x] **Fairness Grader:** Automatic evaluation of disparate impacts across user groups.
- [x] **OpenEnv Compliance:** Standardized `/reset`, `/step`, and `/state` API endpoints.
- [x] **Baseline Agents:** Both rule-based and LLM-capable moderation policies included.
- [x] **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.