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!
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
- 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
- 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
- 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
-
Sample Selection
- Review candidate samples for unlearning
- Each sample shows key demographic information
- Consider potential ripple effects before choosing
-
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
-
Monitor Impact
- Watch performance changes across groups
- Compare with reference retraining results
- Look for unexpected demographic impacts
- Successfully make the model forget targeted samples
- Maintain balanced performance across demographics
- Minimize accuracy drop on unrelated groups
- Find optimal unlearning parameters for different scenarios