The Algorithmic Bias Simulator allows you to explore how machine learning systems can perpetuate and amplify existing social biases when trained on historical data that contains those biases.
How It Works
This simulator uses a simplified model of how recommendation and decision-making algorithms operate in various domains, from hiring to lending to content recommendation. By adjusting different parameters, you can see how changes in the training data, optimization goals, and feedback loops affect outcomes across different demographic groups.
Interactive Elements
Use the controls below to experiment with different scenarios:
Experiment Interface
Simulation results will appear here
Simulation results will appear here
Understanding the Results
The simulation demonstrates how algorithmic systems can produce different outcomes for different demographic groups, even when the algorithm itself contains no explicit bias.
Key factors that influence bias in algorithmic systems include:
- Historical data that reflects past discrimination
- Optimization goals that prioritize certain outcomes over fairness
- Feedback loops that reinforce existing patterns
- Proxy variables that correlate with protected characteristics
This experiment is designed to help users understand these dynamics and consider the ethical implications of algorithmic decision-making systems.