What is AI Bias?
AI bias occurs when machine learning systems produce systematically unfair outcomes for certain groups. Biases can arise from training data, model design, or deployment context.
Sources of Bias
Where bias enters AI systems.
Training Data
Historical biases in the data are learned by the model.
Label Bias
Human annotators introduce their own biases.
Selection Bias
Training data doesn't represent the deployment population.
Measurement Bias
Proxies used for measurement encode bias.
Types of Bias
Common categories of bias in AI systems.
Stereotyping
Reinforcing harmful stereotypes about groups.
Erasure
Underrepresenting or ignoring certain groups.
Disparate Impact
Different outcomes for different groups.
Mitigation Strategies
Approaches to reduce bias.
Diverse Data
Ensure training data represents all relevant groups.
Bias Auditing
Systematically test for bias across demographics.
Fairness Constraints
Incorporate fairness metrics into training.
Bias Detection Demo
Explore how bias manifests in model outputs
Test Input
Enter text to analyze for potential bias
This is a simplified demonstration. Real bias detection requires comprehensive testing across demographics, expert review, and continuous monitoring. AI systems can have subtle biases that simple heuristics won't catch.
Key Takeaways
- 1Bias is often inherited from training data
- 2Different fairness metrics can conflict—choose carefully
- 3Regular auditing is essential for deployed systems
- 4Bias mitigation is an ongoing process, not a one-time fix