Ignorance isn't bliss

How human intuitions about AI can lead to unfair outcomes

Societies are increasingly, and legitimately, concerned that automated decisions based on historical data can lead to unfair outcomes for disadvantaged groups. One of the most common pathways to unintended discrimination by AI systems is that they perpetuate historical and societal biases when trained on historical data. This is because an AI has no wider knowledge to distinguish between bias and legitimate selection.

In this post we investigate whether we can improve the fairness of a machine learning model by removing sensitive attribute fields from the data. By sensitive attributes we mean attributes that the organisation responsible for the system does not intend to discriminate against because of societal norms, law or policy — for example, gender, race, religion.

Read the full explainer here.