What Is...
Governance Concept
What Is AI Bias?
AI bias is not a single phenomenon — it encompasses multiple distinct types with different causes, different tests, and different legal implications. Disparate impact bias is actionable under anti-discrimination law without proof of discriminatory intent.
Four types of AI bias
Historical bias
Training data reflects historical discrimination — the model learns and perpetuates discriminatory patterns from the past.
Representation bias
Training data underrepresents certain groups — the model performs worse for those groups in production.
Proxy variable bias
The model uses variables correlated with protected characteristics to produce discriminatory outcomes without explicitly using those characteristics.
Feedback loop bias
The model's outputs feed back into training data, amplifying initial biases over time.
Legal implications
Disparate impact — where an apparently neutral AI produces outcomes that disproportionately disadvantage a protected group — is actionable under anti-discrimination law in Australia, the EU, UK, and US without proof of discriminatory intent.
Read the legal analysis