Update README.md

README.md

@@ -1,2 +1,54 @@
-# The-Unlearning-Protocol
-Choose which data to make your model forget (Unlearn!), but watch out - every deletion ripples!
+# 🧠 The Unlearning Protocol Game
+![demo-unlearning](/assets/unlearn_demo.gif)
+
+An interactive game that demonstrates machine unlearning through a neural network trained on demographic data. Experience firsthand how making models forget affects their behavior across different population groups!
+
+## 🎯 Educational Goals
+This game helps players understand:
+- The concept and challenges of machine unlearning
+- How selective forgetting impacts model fairness
+- Ripple effects across demographic groups
+- The delicate balance between forgetting and maintaining performance
+
+## 🎮 How It Works
+
+### 1. The Forgetting Process
+- Select individual data points for the model to forget
+- Configure unlearning parameters (learning rate and epochs)
+- Watch how forgetting ripples through the model's behavior
+- Monitor performance changes across different demographics
+
+### 2. Impact Visualization
+- **Real-time Performance Tracking**: See how unlearning affects model accuracy
+- **Demographic Impact**: Monitor changes across age, education, and work hours
+- **Comparative Analysis**: Compare unlearning vs retraining results
+- **Global Statistics**: Track overall model health
+
+### 3. Strategic Elements
+- Choose which samples to forget wisely - not all forgetting is equal!
+- Balance aggressive vs gentle unlearning through parameter tuning
+- Monitor unintended consequences across different population groups
+- Aim for minimal collateral damage while achieving targeted forgetting
+
+## 🎲 How to Play
+
+1. **Sample Selection**
+   - Review candidate samples for unlearning
+   - Each sample shows key demographic information
+   - Consider potential ripple effects before choosing
+
+2. **Configure Unlearning**
+   - Adjust the learning rate (0.001 to 0.1)
+   - Set the number of unlearning epochs (1 to 50)
+   - Higher values = more aggressive forgetting
+
+3. **Monitor Impact**
+   - Watch performance changes across groups
+   - Compare with reference retraining results
+   - Look for unexpected demographic impacts
+
+## 🎯 Challenge Goals
+1. Successfully make the model forget targeted samples
+2. Maintain balanced performance across demographics
+3. Minimize accuracy drop on unrelated groups
+4. Find optimal unlearning parameters for different scenarios