AI Risk Demonstrations
Experience AI risks firsthand through interactive demonstrations. Understand vulnerabilities, attack vectors, and effective mitigation strategies for real-world AI systems.
Data Quality & Privacy Risks
Training Data Poisoning
Interactive demonstration showing how malicious data can compromise model behavior and decision-making, with real-time impact visualization.
What you'll learn:
- • How poisoned data affects model outputs
- • Detection techniques and monitoring
- • Mitigation strategies and controls
Privacy Leakage Detection
Demonstrate how ML models can inadvertently expose sensitive information through inference attacks and membership inference.
Planned features:
- • Membership inference attacks
- • Model inversion techniques
- • Differential privacy solutions
Data Drift Simulation
Visualize how changing data distributions affect model performance over time with interactive drift scenarios.
Planned features:
- • Covariate shift demonstration
- • Concept drift scenarios
- • Monitoring and alerting systems
Model Performance & Security Risks
Adversarial Attack Simulation
Interactive demonstration of adversarial examples that fool AI models with imperceptible input modifications.
Planned features:
- • FGSM and PGD attacks
- • Defense mechanisms
- • Robustness evaluation
Bias Detection & Mitigation
Explore how algorithmic bias emerges and impacts different groups with interactive fairness metrics and mitigation techniques.
Planned features:
- • Bias detection metrics
- • Fairness constraints
- • Demographic parity analysis
Explainability Techniques
Interactive exploration of model interpretability methods including LIME, SHAP, and attention visualization techniques.
Planned features:
- • LIME explanations
- • SHAP value visualization
- • Feature importance ranking
Want to see more demonstrations?
We're continuously developing new interactive risk demonstrations. Request specific scenarios or contribute ideas for future demonstrations.