Computer Vision Foundations · free preview
Measuring and Fixing Algorithmic Bias
How facial analysis performance varies across demographic groups, how dataset bias produces it, and how generative models and counterfactual probes attack it.
1. Vision systems in society
Vision systems in society
2. Facial analysis across demographic groups
Facial analysis across demographic groups
3. Where the bias comes from
Where the bias comes from
4. Generating the missing data
Generating the missing data
5. Why report both error types per group
A face recognition vendor reports 99% top-line accuracy for its system. Why does this single number fail as evidence of fairness across demographic groups?
6. The factor between groups
In a vendor test, one demographic group shows a false positive rate of for a given face recognition system. Another group shows a false positive rate that is a factor of 100 higher. Write the higher false positive rate as a decimal.
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