Bias in AI
Systematic errors in AI systems that produce unfair outcomes, often reflecting historical prejudices in training data or flawed assumptions in model design.
Types of Bias
Historical bias: Training data reflects past discrimination. Representation bias: Certain groups underrepresented in data. Measurement bias: Flawed proxies for what we want to measure. Aggregation bias: One model for populations that differ.
Real-World Impact
Biased hiring algorithms discriminating against women. Facial recognition performing poorly on darker skin tones. Criminal risk assessment tools showing racial bias. Healthcare algorithms underserving minority patients.
Mitigation Strategies
Diverse and representative training data. Fairness-aware training objectives. Regular bias audits and testing across demographic groups. Transparency in model decisions. Including affected communities in development.